Hire an AI & ML engineer to ship production systems

Custom AI development that ships to production

Design and build production AI systems: assistants, agents, RAG apps, fine-tuned models, and ML APIs for startups and enterprises.

• Architecture and build for LLM apps, agents, and ML services
• Integration with OpenAI, Anthropic, Hugging Face, Bedrock, or self-hosted models
• Backend APIs with FastAPI, Docker, and cloud deployment on AWS
• Evaluation, guardrails, and monitoring so systems stay reliable after launch

Production AI agents with tools, memory, and control

Autonomous LLM agents that call tools, follow multi-step plans, and run securely — including MCP integrations, LangGraph workflows, and SmolAgent systems.

• Tool-using agents with MCP, APIs, databases, and internal systems
• LangGraph / LangChain orchestration and structured outputs
• Locally hosted or cloud agent runtimes for sensitive data
• Guardrails, logging, and regression checks for agent reliability

Production RAG systems connected to your data

Retrieval-augmented generation from docs, ERP, PostgreSQL, or MongoDB — with hybrid search, embeddings, vector databases, and reranking.

• Hybrid BM25 + dense retrieval and chunking strategies
• Qdrant / pgvector indexes with Cohere or cross-encoder rerank
• Redis caching and low-latency query paths
• CRAG-style corrective loops and evaluation with Ragas-style metrics

Domain-ready models with LoRA / QLoRA

Fine-tune open-source LLMs for your tone, domain, and tasks — efficient PEFT workflows on Hugging Face with evaluation, export, and deployment support.

• Dataset prep, cleaning, and instruction formatting
• LoRA / QLoRA / PEFT training for Mistral, Llama, and similar models
• Preference-style alignment workflows where needed
• ONNX / quantized export paths for faster inference

Reliable ML systems from data to inference

Shashi Ranjan is an ML Engineer as well as an AI engineer — delivering classical and deep learning systems for classification, regression, forecasting, ranking, and recommendation, with production-grade training, registry, and monitoring.

• Feature engineering and tabular ML (XGBoost, LightGBM, Scikit-Learn, PyTorch)
• Model development: ANNs, gradient boosting, Optuna hyperparameter search, offline evaluation
• Training pipelines with Airflow, Spark, Kafka, and Ray where needed
• MLflow model registry, continuous training, and A/B rollout patterns
• Drift monitoring and batch or online inference services
• Analytics & decision systems — dashboards, SQL automation, and client ML delivery from consulting work

Ship LLM apps with evals and guardrails

Turn fragile demos into measurable systems — offline/online eval harnesses, tracing, experiment tracking, safety filters, and cost/latency monitoring.

• Ragas / DeepEval-style evaluation suites and regression tests
• LangSmith tracing and W&B experiment tracking
• Moderation, PII redaction, and prompt-injection defenses
• Latency, cost, and quality dashboards for production LLM traffic

Who this is for

• Founders hiring a freelance ML engineer for pipelines, model training, and production inference
• Teams that need an AI engineer for agents, RAG, or fine-tuned LLMs — without agency overhead
• Product groups upgrading demos into evaluated, monitored systems
• Companies seeking remote AI / ML specialist help from Bengaluru