senior AI/ML engineer

Hiringhood

Chennai, Tamil Nadu, India

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

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

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

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