AI/ML Engineer

tecHindustan Solutions Private Limited; Mohali; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

tecHindustan Solutions Private Limited is seeking an AI/ML Engineer to design and deploy intelligent systems across the full lifecycle—from model development to production. You will build GenAI solutions, leverage embeddings and vector databases, and develop scalable REST APIs in a collaborative team.

Ideal candidates have 3+ years of AI/ML development with strong Python, NLP, and experience with LangChain/LlamaIndex in GenAI contexts. Familiarity with ETL, CI/CD, and Agile practices is a bonus.

Qualifications

• 3+ years of AI/ML development with Python.
• Hands-on with NLP, GenAI, embeddings, and semantic search.
• Experience deploying models and building scalable AI services.
• Familiarity with RAG pipelines, vector databases, and LLM APIs.

Responsibilities

• Design, develop, and maintain ML/AI solutions using Python.
• Build and deploy GenAI systems: LLMs, RAG pipelines, agentic workflows.
• Work with embeddings, semantic search, vector databases, MCP tool integrations.
• Develop and integrate RESTful APIs for AI/ML services.
• Fine-tune and evaluate models; build evaluation frameworks for accuracy and bias.
• Optimize for performance, latency, cost, and scalability.
• Collaborate cross-functionally; write clean, testable code; participate in code reviews.
• Stay current with model releases and assess usability vs. hype.

Skills

Python programming

Classical ML

Deep learning

NLP / Generative AI

Embeddings & vector databases

RAG / LangChain / LlamaIndex

API development (REST)

Model evaluation & bias

Git / CI/CD / Agile

LLM APIs (OpenAI/Anthropic)

Tools

LangChain

LangGraph

LlamaIndex

Pinecone

Weaviate

Qdrant

PostgreSQL

MongoDB

Job description

We’re looking for an AI/ML Engineer with experience building and deploying intelligent systems, spanning classical ML, deep learning, statistics, and modern Generative AI, across the full solution lifecycle from model development to production deployment.

Responsibilities

• Design, develop, and maintain ML/AI solutions using Python
• Build and deploy GenAI systems: LLMs, RAG pipelines, and agentic workflows (LangChain, LlamaIndex, LangGraph)
• Work with embeddings, semantic search, vector databases, and MCP-based tool integrations
• Develop and integrate RESTful APIs for AI/ML services
• Fine-tune and evaluate models (including LoRA/QLoRA) and build evaluation frameworks for accuracy, reliability, and bias
• Optimize for performance, latency, cost, and scalability
• Collaborate cross-functionally, write clean/testable code, participate in code reviews
• Stay current with new model releases, techniques, and tools, and judge what's actually worth adopting vs. hype

Requirements

• 3+ years in AI/ML development, strong Python
• Solid grasp of classical ML (regression, classification, clustering) and foundational statistics (probability, hypothesis testing, A/B testing)
• Understanding of deep learning concepts (neural network architectures, CNNs, RNNs, transformers, backpropagation, training/optimization techniques)
• Hands-on with NLP, Generative AI, embeddings, semantic search
• Experience with RAG, vector databases (Pinecone/Weaviate/Qdrant), and PostgreSQL/MongoDB
• Experience with agentic frameworks (LangChain, LangGraph, LlamaIndex) and MCP
• Familiarity with Git, CI/CD, Agile
• Experience with OpenAI, Anthropic, or similar LLM APIs
• Bonus: LoRA/QLoRA fine-tuning, reasoning/test-time-compute techniques, LLMOps

Stand out if you can

• Have taken at least one GenAI project end-to-end, from idea to a live, hosted deployment someone else can actually use (a multi-agent system, an AI product, or any other practical application), not just notebooks or coursework
• Demonstrate awareness of current AI trends and practical judgment on what's worth adopting
• Show sound reasoning around model selection and architecture tradeoffs for a given use case