senior AI/ML engineer

Roles & Responsibilities

• Design, develop and deploy AI/ML and Generative AI solutions from concept to

production.

• Build applications using LLMs, SLMs, RAG, GraphRAG, AI agents, NLP and machine-

learning models.

• Develop agentic workflows involving tool calling, planning, memory, routing, human-in-

the-loop controls and multi-agent orchestration.

• Build and optimize retrieval pipelines using embeddings, semantic search, hybrid search,

vector databases, reranking and metadata filtering.

• Integrate AI solutions with enterprise platforms, databases, APIs, healthcare systems and

third-party applications.

• Evaluate and select the right approach across traditional ML, LLMs, SLMs, prompt

engineering, fine-tuning, RAG and deterministic workflows.

• Build evaluation frameworks to measure accuracy, groundedness, hallucination, retrieval

relevance, latency, safety and cost.

• Implement AI guardrails, structured outputs, confidence scoring, fallback mechanisms

and auditability.

• Create scalable APIs and microservices for AI-powered products.

• Implement MLOps and LLMOps practices, including model and prompt versioning,

monitoring, tracing, CI/CD and production observability.

• Optimize model performance, token consumption, inference cost, response time and

infrastructure usage.

• Translate business requirements into technical solutions, architecture, delivery plans and

measurable outcomes.

• Participate in product discovery, customer discussions, technical demonstrations and

solution reviews.

• Mentor junior engineers and contribute to AI engineering standards and reusable

frameworks.

Must have:

Healthcare domain experience is must

Experience: 4–8+ years in AI/ML | Applied ML | Data Science | Software Engineering

Python

Machine Learning

LLM Fine-tuning

Deep Learning

NLP

FastAPI | Flask

REST APIs | Microservices

SQL

Structured | Unstructured Data Processing

Production-quality coding

Production-grade LLM / GenAI Applications

RAG | Embeddings | Vector Search

Semantic Search | Hybrid Search

Reranking

AWS | Azure | GCP

Agentic AI

Tool Calling | Function Calling

Human-in-the-loop

Multi-agent Workflows

AI Evaluation

AI Monitoring

AI Security

Scalability

Latency Optimization

Cost Optimization

LangChain | LangGraph | LlamaIndex | Semantic Kernel | AutoGen | CrewAI | Equivalent

OpenAI | Azure OpenAI | Anthropic Claude | AWS Bedrock | Google Vertex AI | Hugging Face | Llama | Mistral

Pinecone | Qdrant | Weaviate | Milvus | FAISS | pgvector | Elasticsearch | OpenSearch

Good to have:

LoRA | QLoRA | PEFT

Small Language Models (SLMs)

Open-source Model Deployment

MLOps | LLMOps

Model/Prompt Versioning

Monitoring | Tracing | Production Observability

AI Evaluation Frameworks

Accuracy | Groundedness | Hallucination | Retrieval Relevance | Latency | Safety | Cost Evaluation

AI Guardrails

Auditability

Token Optimization

Inference Cost Optimization

Response Time Optimization

Infrastructure Optimization

Multiple AI/ML/GenAI Projects

Multiple Business Domains

Product-based Company / Product Organization

AI Product Development: Concept → Production

Enterprise AI Integrations

Databases | APIs | Third-party Applications

AI Architecture | Solution Design

Customer Discussions

Technical Demonstrations

Mentoring Junior Engineers

Reusable AI Engineering Frameworks | Standards