When you don’t have in-house ML expertise but need reliable forecasts to make decisions faster, our developers build models that identify patterns, trends, and risks with precision.
What we deliver:
- Time-series forecasting (Prophet, LSTM)
- Demand & sales forecasting
- Risk scoring and anomaly detection
- Behavioural modelling
A consumer retail client improved inventory accuracy after adopting our forecasting pipeline, powered by LSTM + Prophet and tailored feature engineering.
If your teams spend hours reviewing documents, extracting information, or categorizing text, our NLP developers build pipelines that take over the repetitive work.
What we deliver:
- Text classification, entity extraction, summarization
- Contract/document tagging
- Sentiment & intent analysis
- Search & semantic retrieval
A legal tech client automated contract tagging and summarization using a transformer-based pipeline, reducing manual review time significantly.
When manual inspection is slow or error-prone, we build you models that see what humans miss, keeping speed and scale in mind.
What we deliver:
- Defect detection & quality inspection
- Object detection (YOLOv8)
- Custom CNN models
- OCR & document vision workflows
A manufacturing client integrated our defect-detection model into their line and saw fewer missed defects and faster inspection cycles.
If you want your users to discover the right content or products at the right time, our team develops AI-led recommenders that learn from behaviour.
What we deliver:
- Collaborative filtering
- Content-based recommenders
- Hybrid recommendation systems
- Personalized ranking models
An e-commerce client saw a noticeable lift in average order value after adopting our recommendation engine.
When support teams are stretched and response times matter, our AI consultants build assistants that understand context, not mere keyword scripts.
What we deliver:
- LLM-powered chatbots
- Rasa-based conversational flows
- Voice bots & customer support agents
- Workflow-triggered automation via chat
A healthcare provider reduced frontline load after implementing our triage assistant built using Rasa + LLM integrations.
If you’re exploring GenAI but worried about privacy, accuracy, or domain-specific behaviour, we customize models around your data for seamless AI Integration. No more relying on generic internet text.
What we deliver:
- Fine-tuning open-source models (Llama 3, Mistral, etc.)
- Retrieval-augmented generation (RAG)
- Domain-specific copilots (finance, compliance, legal, healthcare)
- On-prem or VPC-secure deployments
Clients across finance and support operations saw more accurate responses and reduced dependency on manual reviews after implementing domain-trained LLMs.
Not sure which AI capability aligns with your use case?
Reusable AI Accelerators that Cut Your Time-to-Value
Building AI from scratch is slow and resource-heavy.
Our developers use internally built AI accelerators to speed up PoCs, stabilize pipelines, and deliver sustainable systems without compromising quality or explainability.
These accelerators aren’t off-the-shelf packages. They’re engineering frameworks refined across real-world projects to help your team move from idea to deployment in weeks.
AEGIS-NLP Core
A ready-to-use foundation for text-heavy workflows.
It streamlines preprocessing, entity extraction, text classification, summarization, and evaluation, offering a stable starting point for contract analysis, IDP, ticket triage, and domain-specific NLP systems.
Impact:
Cuts NLP development time by up to 40% in early stages.
VisionFlow
A modular workflow for building and validating computer vision models.
It includes components for dataset management, data augmentation, annotation tools, model checks, and performance benchmarking. An ideal accelerator for defect detection, OCR, and visual QA use cases.
Impact:
Helps teams iterate quickly while maintaining consistent accuracy and model quality.
AutoML Bridge
An internal wrapper around trusted libraries (Hugging Face, AutoGluon) to automate early-stage model exploration.
It doesn’t replace custom modeling but speeds up baselining, enabling our developers to identify promising algorithms and configurations quickly.
Impact:
Reduces PoC cycle time and helps validate feasibility early.
Skills & Tech Stack You Get Access To
When you hire remote AI developers from Aegis Softtech, you get engineers who are comfortable across the full modern AI stack. Soon after we discuss your needs, we will already know which tools would work best for your project.
Here’s the entire tech stack and skills we have expertise in:
Our developers work with proven, production-grade frameworks used across enterprise AI systems.
- Python (primary language for ML, NLP, CV, and LLM workflows)
- PyTorch, TensorFlow, Keras for deep learning
- Scikit-learn for classical ML
- Node.js/JavaScript for integrating AI features into frontends and microservices
Robust frameworks for text-heavy workflows, Generative AI apps, and domain-specific LLM projects.
- Hugging Face Transformers
- LangChain, LlamaIndex for orchestration and RAG pipelines
- spaCy, NLTK
- Llama 3, Mistral, GPT, Claude, Gemini for API-based and on-prem deployments
- Stable Diffusion APIs for image generation and creative workflows
For building enterprise-grade search, assistants, and knowledge systems.
- Pinecone
- Weaviate
- FAISS
Our developers can deploy, scale, and manage AI workloads on your preferred cloud.
AWS
- SageMaker (training + deployment)
- S3 (data lake)
- Lambda (serverless inference)
Azure
- Azure ML, Cognitive Services
- Azure OpenAI integration
Google Cloud
- Vertex AI
- BigQuery ML
- Cloud Run for lightweight serving
Hybrid/On-Prem
- Kubernetes on EKS, AKS, GKE, or self-managed clusters
We build Enterprise AI solutions you can monitor, retrain, and scale without reinventing infrastructure.
- MLflow for experiment tracking & model registry
- Kubeflow for ML pipelines
- Apache Airflow for workflow orchestration
- GitHub Actions, Argo CD for CI/CD
- Docker, Kubernetes for containerization & scalable deployments
For embedding AI into existing business systems.
- Snowflake, Databricks for large-scale data + ML
- Salesforce, SAP
- Twilio, Stripe
- REST/gRPC microservices & custom APIs
How We Build AI Solutions: End-to-End Engineering Process
AI projects fail when they’re treated like experiments instead of engineered systems. Our developers follow a structured, production-ready approach that covers everything from data foundations to long-term model performance.
We take full responsibility for building a stable and scalable system for you. And, here’s how we do it:
Data Pipeline Engineering
Every project begins with getting the data foundations right. We set up clean, reliable pipelines using tools like Apache Airflow, ensuring all ingested data is validated, transformed, and versioned with DVC.
It creates a reproducible and trustworthy flow of data that models can depend on, and provides full visibility across the pipeline.
Model Development & Validation
Once the data is structured, we move into iterative experimentation. Developers explore multiple algorithms, test architectural variations, and benchmark results using cross-validation and controlled A/B tests.
Every model is evaluated on clarity, precision, recall, F1 score, and real-world behaviour. Hence, the developed model is thoroughly stress-tested before it is deployed in your environment.
Deployment & Serving
Our team packages models in Docker, builds optimized serving layers, and deploys them via Kubernetes, serverless platforms like Lambda or Cloud Run, or REST/gRPC APIs, depending on your stack.
It ensures your AI system is accessible, maintainable, and capable of handling traffic from the get-go.
Monitoring, Drift Detection & Lifecycle Management
We prepare for any potential model degradation (over time) from the start. Our developers set up monitoring pipelines using Prometheus and Grafana, track model drift through Evidently AI, and maintain audit logs for every prediction.
When performance shifts, retraining triggers or model updates are rolled out safely, keeping your AI reliable long after deployment.
Security, Governance & Documentation
Every stage of our process is built on secure, compliant engineering practices. From RBAC and VPC isolation to GDPR/CCPA-aligned data handling, we ensure your system meets enterprise-grade governance standards.
You will find documentation for everything—Swagger APIs, model cards, and architecture details—so your team always has clear visibility.
Why Hire Offshore AI Developers from Aegis Softtech
You need AI engineers and experts who understand how the technology actually behaves in production. They must know what behavioral changes occur within real workflows, under practical constraints, with real data.
And that’s exactly what we vouch for.
Cross-Domain Engineering Experience
You will work with experts who have hands-on experience across various industries, like healthcare, finance, retail, logistics, manufacturing, and legal tech. They understand the nuances of each domain—the constraints, the compliance requirements, the edge cases.
So, they ask the right questions and develop solutions that suit your business, rather than forcing generic models into your workflows.
Faster Delivery with In-House AI Accelerators
With reusable frameworks like AEGIS-NLP Core, VisionFlow, and AutoML Bridge, our teams don’t start from a blank slate. These accelerators shorten PoC timelines, stabilize early-stage pipelines, and help us validate feasibility faster.
As a result, your project moves from idea to working prototype in significantly less time than traditional development cycles.
Built-In MLOps from Day One
For us, deployment is never an afterthought. We embed versioning, reproducibility, CI/CD, monitoring, and drift detection from the start using tools like MLflow, Kubeflow, Airflow, Prometheus, Grafana, and Evidently AI.
You don’t get a one-off model; you get an AI system engineered to last.
Reliable Performance and Measurable Outcomes
Our developers are trained to build models that reflect real-world behaviour. They design for clarity, interpretability, and business relevance, focusing on accuracy where it matters, latency where it counts, and stability under real workloads.
All these strategic planning translates into dependable systems your team can trust, maintain, and scale.
Access to a Full Engineering Ecosystem
When you hire an AI/ML developer from us, you also get the advantage of our broader engineering bench—data engineers, cloud architects, MLOps specialists, DevOps, and QA—available whenever your project needs them.
It reduces dependency on multiple vendors and provides a coordinated, end-to-end delivery capability without requiring the establishment of an entire internal team.
Long-Term Reliability and Stable Engagements
We provide predictable, stable AI talent—not rotating freelancers or short-term contractors. Our developers integrate into your team, follow your processes, and stay aligned for the duration of your project roadmap.
You get continuity, reliability, and a consistent development velocity across months—as long as your project demands.
Domain-Trained AI Developers for Your Industry
Our developers understand these nuances and build systems that align with the way your business operates.
Here’s how we’ve applied AI across key industries.
Healthcare & Life Sciences
Our developers build solutions that streamline clinical workflows, improve data accuracy, and support faster decision-making.
It includes intelligent document processing for patient records, automated claim extraction, medical triage chatbots, and privacy-conscious LLMs aligned with healthcare compliance standards.
BFSI & Fintech
Our AI Consulting services help financial teams build fraud detection engines, risk scoring models, secure LLM assistants, and analytics systems that operate reliably under strict regulatory controls. From anomaly detection to transaction intelligence, our developers create AI pipelines built for accuracy, auditability, and scale.
Retail & E-Commerce
Our teams build recommendation engines, demand forecasting models, and personalization workflows that improve customer experience and operational planning.
Whether it’s dynamic product suggestions or forecasting seasonal demand shifts, we build systems that help retail and e-commerce teams respond faster and smarter.
Logistics & Supply Chain
When integrating AI in the logistics industry, we develop forecasting, route optimization, anomaly detection, and operational visibility systems that help teams stay ahead of disruptions.
From predictive delays to intelligent routing, we design models that reduce bottlenecks and keep supply chains moving efficiently.
Manufacturing & Industrial Automation
We build computer vision systems for defect detection, inspection workflows, and real-time quality control. Our models are trained on domain-specific datasets and integrated into on-prem or edge environments, delivering consistent performance on the factory floor.
LegalTech & EdTech
From document classification and contract summarization to automated tagging, semantic search, and domain-trained LLMs, our developers support businesses built around heavy, unstructured data. We create AI systems that save time, improve accuracy, and make information easier to navigate.
Our Team
Your AI Developer Backed by a Complete Engineering Ecosystem
Hiring one AI developer is often the starting point. As your AI roadmap expands—new models, new data sources, deployment needs, or MLOps requirements—you shouldn’t have to find new vendors or rebuild context from scratch.
With Aegis Softtech, your developer is supported by a broader engineering ecosystem that can step in whenever the project calls for it.
Access to Specialized AI, Data & Cloud Expertise
The developer doesn’t work in isolation. Our architects, data engineers, MLOps specialists, DevOps, and QA teams are available to support design decisions, infrastructure setup, pipeline optimization, or production rollout without additional onboarding cycles.
End-to-End Delivery Support When Your Scope Grows
Suppose your project expands from a model to a full product. In that case, we can assemble a Delivery Pod that includes the right mix of roles—AI developers, data engineers, cloud specialists, and testers. You get coordinated execution across the entire lifecycle rather than piecemeal talent.
Seamless Scaling Without Re-Explaining Requirements
Because the Delivery Pod already understands your architecture and workflows, scaling your team becomes frictionless. Adding more hands doesn’t slow things down. The new contributors work with the same standards, documentation, and context from the very beginning.
One Partner for Your Entire AI Roadmap
Whether you continue with a single developer or grow into a full multi-role team, we bring you continuity, predictability, and long-term reliability—the opposite of fragmented contractors or talent marketplaces.
Add data engineers, MLOps, or cloud specialists as needed for the project.
How Our Hiring Process Works
We keep hiring simple, fast, and predictable, so you get the right AI developers without weeks of back-and-forth.
Share Your Requirements
Tell us what you need: skills, tech stack, project goals, timeline, and any domain specifics. It gives us the context to match you accurately.
Receive Curated Developer Profiles
We shortlist the most relevant AI developers from our team and send their CVs for your review. You will receive only candidates who exactly match your requirements.
Shortlist Your Preferred Candidates
You evaluate the profiles and select the developers you want to speak with. Take as much time as you need; there’s no pressure.
Interview the Shortlisted Developers
We schedule interviews at your convenience, giving you facetime to assess technical depth, communication, and alignment with your team’s workflows.
Start Working with a 7-Day Free Trial
Once you hire, your developer begins immediately—backed by a 7-day free trial. You evaluate fit, working style, and delivery quality before making a long-term commitment.
Start working with an AI developer, see how they collaborate with your team, and continue only if the fit is right.
How to Build an AI Data Center: Cost, Power & Design
Generative AI in Software Development
Generative AI in Legal Industry
Engagement & Hiring Models
AI initiatives differ widely in scope, pace, and complexity.
No matter which model you choose, your AI developer comes with the same engineering foundation—clean documentation, secure environments, and versioned workflows. You also have access to our broader AI, Data, and Cloud teams when needed.
The engagement structure may change, but the quality, maturity, and reliability remain the same.
Dedicated Monthly Model
Ideal for long-term roadmaps, product builds, and steady engineering support.
You get a full-time AI developer who works exclusively on your project, follows your processes, and integrates with your internal team. This model ensures continuity, deep context, and consistent delivery velocity — without the time and overhead of hiring in-house.
It comes with a fixed pricing of $2800 per resource per month.
Project-Based Implementation Model
Designed for end-to-end AI initiatives where you want a complete implementation team rather than individual contributors.
We scope the project with you, outline requirements, assign the right mix of AI developers, data engineers, MLOps, and cloud specialists, and deliver the entire solution from discovery to deployment.
Pricing is determined by the project size, complexity, and number of resources involved, ensuring the cost matches the actual engineering effort required.
Time-Based Hourly Model
Perfect for short engagements, experiments, feasibility studies, or temporary bandwidth.
You pay only for the hours you need, making this model ideal for teams exploring AI possibilities, validating ideas, or augmenting their existing engineering staff for a defined period.
The cost is fixed at $20 per hour per resource.
FAQs
Our dedicated monthly model starts at $2,800 per developer per month, and the hourly model starts at $20/hour. Project-based pricing varies depending on scope, complexity, and the number of resources assigned.
Most clients onboard developers within 48 to 72 hours after finalizing interviews. Since our developers are pre-vetted and part of our existing team, there’s no long hiring cycle or waiting period.
Yes. Every engagement begins with a 7-day free trial so you can evaluate fit, communication, and delivery quality before moving forward.
Yes. We have AI developers available across multiple time zones, ensuring coverage for US, Europe, APAC, and Middle East clients.
Our developers work across ML, NLP, LLMs, GenAI, Computer Vision, MLOps, data pipelines, and cloud-native deployments. They’re experienced with tools like PyTorch, TensorFlow, Hugging Face, LangChain, Kubernetes, Airflow, MLflow, and major cloud platforms.
You can start with one developer and scale up at any time. Our broader engineering ecosystem—architects, data engineers, MLOps, DevOps, and QA—can be expanded as your project grows.
All work follows strict security controls, including NDA, RBAC, encrypted environments, and GDPR/CCPA alignment. IP always stays with you.
They can do both. Developers integrate into your team’s workflow or collaborate through our Delivery Pod, depending on what your project needs.
Yes. Our hourly model is ideal for PoCs, experiments, audits, and short cycles where you need temporary AI expertise.
If you’re not satisfied during the 7-day trial, we replace the developer or adjust the engagement—no cost and no complications.