Our AI and ML engineers build production-grade intelligent systems, LLM apps, RAG pipelines, deep learning models, and ML inference APIs across Python, PyTorch, and TensorFlow, for teams embedding AI into existing platforms or building net-new products.
- Production-Grade AI Systems Built for Enterprise and Product Teams
- Deep Engineering Capability Across LLMs, RAG Pipelines, and ML Model Deployment
- Flexible Engagement like Full-Time, Part-Time, or Trial Before You Commit
A Mumbai-Based AI Engineering Team Serving Global Markets
We are a product-focused software development company building AI-powered applications, machine learning systems, and intelligent data infrastructure for businesses across the US, UK, and international markets, with experience as long-term engineering partners, not one-time project vendors.
Our AI engineers work directly within active product environments where model reliability, inference performance, and long-term maintainability play a critical role in how AI features scale and how teams avoid rebuilding systems when data distributions shift or product requirements change.
- AI and ML engineering experience across 20+ business and technology sectors
- Delivery workflows built for distributed, fast-moving product teams
- Long-term support across LLMs, deep learning models, data pipelines, and cloud AI infrastructure
How We Onboard Your AI/ML Developer
Most AI development engagements lose weeks in tooling setup, model access approvals, and infrastructure alignment before any meaningful work begins. Ours is built to move faster from requirements to active development in four structured steps, with no unnecessary stages between you and a developer working directly on your AI product.
Requirements & Alignment
Your AI goals, data availability, model constraints, and stack. We match you to the right profile, like an LLM engineer, ML specialist, or MLOps engineer.
Developer Matching
We shortlist engineers by stack fit (Python, LangChain, PyTorch, TensorFlow), domain experience, and the AI problem being solved.
Trial Engagement
Run a paid trial on real backlog work. The developer reviews your data infrastructure and aligns on model architecture from day one.
Active Development
Sprint planning, experiment tracking, and deployment cycles the developer works inside your workflow as a fully embedded team member.
AI Products We've Built and Shipped
AI Sales Coaching Platform for mple
Mple needed to move beyond traditional sales training by building an AI-driven platform capable of running role-play simulations, analysing conversation quality, and delivering personalised coaching feedback at enterprise scale for clients across pharma, banking, and FMCG.
We built the full AI platform from the ground up, covering LLM-powered role-play simulation, NLP-based conversation analysis, real-time performance scoring, and personalised coaching feedback pipelines, backed by a mobile application, an admin panel, and data infrastructure that support ongoing learning loops across enterprise training cohorts.
The right profile for products that need LLM integration, prompt engineering, RAG pipelines, or agent workflows built on OpenAI, Anthropic, or open-source models.
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Suited for products requiring custom model training, LoRA-based fine-tuning, neural network architecture, or inference APIs for classification, forecasting, computer vision, or NLP tasks.
Best fit for teams that need MLOps practices for model deployment pipelines, experiment tracking, versioning, and cloud-based serving infrastructure on AWS, GCP, or Azure.
Production-Tested AI Delivery Background
Our team has shipped AI systems for Midgenie, an AI video dubbing platform with custom lip-sync models and multi-language TTS, and mple, an NLP-powered sales coaching platform used across pharma, banking, and FMCG clients, with live systems serving real production load, not portfolio builds. Teams that have previously worked with agencies that over-promise on AI development will recognise the difference between a team that has handled production model behaviour and one that hasn't.
Senior-Level Technical Accountability
Every engagement is staffed with engineers who carry genuine seniority in applied AI, not junior resources managed by a senior who attends your calls. The engineer designing your model architecture is the same person in your standups, making infrastructure decisions, and accountable for delivery quality. Seniority is consistently cited as one of the primary advantages businesses gain from outsourcing software development to a specialist AI team, and one of the first things that gets diluted when an agency optimises for margin over quality.
Post-Deployment Model Continuity
Output monitoring, prompt iteration, and model re-evaluation after data drift are part of how we structure engagements, not a separate scope conversation. Teams running models on cloud infrastructure particularly benefit from a developer who already knows the system, rather than onboarding someone new every time a production issue surfaces.
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Industries Covered
Custom LLM-powered applications built on OpenAI, Anthropic, Mistral, or Gemini APIs with structured output handling, tool use, multi-turn conversation management, and production-grade error handling.
Retrieval-augmented generation systems using LangChain or LlamaIndex, with vector databases including Pinecone, Weaviate, or pgvector covering chunking strategy, embedding models and context relevance scoring (code-b.dev/blog/llm-embeddings).
End-to-end model development in Python using PyTorch, TensorFlow, or Scikit-learn from feature engineering and training pipelines through evaluation, hyperparameter tuning, and production-ready inference endpoints.
Neural network architectures built with PyTorch or TensorFlow covering image classification, object detection, sequence modelling, and NLP trained on GPU infrastructure and deployed as optimised inference endpoints.
Autonomous agent systems built with LangChain, CrewAI, AutoGen, or Semantic Kernel covering tool-use definitions, memory management, task planning logic, and safety guardrails for production enterprise workflows.
Text classification, sentiment analysis, named entity recognition, and chatbot systems built for teams managing support automation, feedback analysis, document processing, and message-heavy workflows at scale.
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Image and video processing pipelines covering object detection, quality inspection, document scanning, and visual classification are built for businesses that require fast, accurate visual validation in production.
Model deployment, versioning, and monitoring pipelines using MLflow, Weights & Biases, SageMaker, or Vertex AI, keeping production models evaluated, re-trainable, and observable without manual intervention.
ETL and feature engineering pipelines built on Apache Spark, Airflow, or dbt, transforming raw data into model-ready datasets with automated validation, lineage tracking, and scheduled refresh cycles.
A Dedicated AI/ML Engineer, Fully Embedded in Your Workflow
A full-time AI engineer embedded in your team, working exclusively on your product, in your time zone, within your sprint cadence. Suited for products with ongoing model development, active LLM feature work, or AI systems that need sustained iteration as data and usage evolve.
Model Ownership
The engineer owns the full AI system context from Gen AI model selection and data pipeline architecture to inference endpoints and production monitoring.
Sprint Integration
Works within your existing agile workflow, attends standups, contributes to sprint planning, and ships with your team rather than alongside it.
Context Continuity
Same engineer, same context, every sprint. Model architecture decisions, evaluation history, and data assumptions stay with one person throughout the engagement.
Toolchain Alignment
Use your existing version control, experiment tracking, and communication stack with no separate workflow to manage or reconcile with your permanent team.
Industries Our AI & ML Developers Build Intelligent Systems For
Job titles mean little in AI hiring using a framework in a tutorial is worlds apart from shipping it to production. Hiring from India also means evaluating delivery structure and timezone overlap.
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Built for Every Stage of Your AI Product
The right AI developer profile depends on where your product is right now, not just what you're planning to build next.
The team didn't just execute what was handed to them, and they brought genuine product thinking to every technical decision. Architecture, performance, and user experience were all considered together, not in isolation. What impressed me most was that the quality of the final build exceeded what we had scoped going in.
Startups
Early-stage teams need an AI engineer who makes sound architecture decisions from the start, moves fast without accumulating technical debt, and can own model selection, retrieval strategy, and data structure without requiring a large team around them.
Mid-Size Businesses
Growing product teams need engineers who integrate into an existing codebase, adopt current conventions, and add intelligent capability without destabilising what is already live and serving customers.
Enterprise Level Businesses
Enterprise AI projects require engineers with experience in PII handling, role-based access to model outputs, audit logging, and compliance-aware data pipelines built for regulated or security-sensitive production environments.
They have strong expertise in the latest technologies and provide excellent guidance in using them effectively.
CODE B launches the products quickly, and their solutions have excellent architecture and are scalable.
CODE B is proactive in coming up with solutions.
Aside from getting the job done, they’re able to provide their expertise and share their opinion.
They’re a very bright team that requires minimal levels of communication or time investment to be very effective.
Their constant communication was a key aspect of the success.
They completed the project within the timeline we gave them, and they did it within budget.
Had a great experience working with the team and in times of crisis, CODE B team was always there to support us.
The way that they have supported us by giving us one of their developers to work directly with our development team.
Our overall experience has been very positive.
They are friendly and reliable.
The ability to deliver on time impressed us the most.
They’re excellent at what they do and come up with solutions for various problems.
CODE B will work overtime to resolve issues, which is a difficult trait to find.
Code B’s communicative.
I’ve had a great experience working with CODE B
The main positive point of working with CODE B team is their analyzing skills.
They are receptive and try to adjust to meet our requirements.
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