Artificial Intelligence Engineer

Shell Infotech; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Shell Infotech is seeking an experienced AI Engineer / Data Scientist to join a client project in Bangalore. You will design and build production-grade AI systems focusing on multi-agent orchestration and robust LLM workflows.

You will develop RAG pipelines, semantic search, vector embeddings, and guardrails, while evaluating model quality, latency, and cost. The role demands 6–10 years of hands-on experience and a BE/BTech/MCA or equivalent degree.

Qualifications

• 6–10 years of experience across Data Science, ML, AI Engineering, or related domains.
• Hands-on experience building agentic systems, not just API experiments.
• Ability to design end-to-end AI system architectures.
• Understanding trade-offs between accuracy, latency, cost, reliability, and scalability.
• Interest in solving ambiguous problems and delivering production-ready AI solutions.

Responsibilities

• Architect and build single & multi-agent systems with complex orchestration and workflow design.
• Develop production-grade RAG pipelines, semantic search, vector embeddings, and vector databases.
• Build intelligent workflows covering data ingestion retrieval reasoning tool calling inference response.
• Engineer and optimize prompts, context strategies, and agent workflows for accuracy, reliability, and performance.
• Design custom domain-specific guardrails to handle complex business scenarios.
• Build robust LLM evaluations to measure quality, accuracy, hallucination, and task performance.
• Debug and trace agentic workflows using MLflow, LangSmith, or similar observability/evaluation platforms.
• Continuously evaluate emerging AI frameworks and technologies for production use.

Skills

Python

LangChain

LangGraph

LLMs & Prompt Engineering

Vector Embeddings

Semantic Search

Multi-Agent Orchestration

Guardrails

LangSmith/MLflow/Evals

Education

BE/BTech/MCA or equivalent degree

Tools

Python

LangChain

LangGraph

Vector Databases

Job description

Hi Everyone,

Hope youre doing well!

We have an urgent opening for a AI Engineer / Data Scientist one of our esteemed clients.

Client Location: Bangalore

Employment Type: Full Time

Experience Required: 6 – 10Years (Immediate to 15Days Only)

Education: BE/BTech/MCA or equivalent degree

Job Description-

Are you building AI agents—not just API wrappers?

We’re looking for an Agentic AI Engineer / Data Scientist who loves designing intelligent, production-grade systems and turning LLM capabilities into reliable enterprise solutions. What You’ll Work On Architect and build single & multi-agent systems with complex orchestration and workflow design.

Develop production-grade RAG pipelines, semantic search, vector embeddings, and vector databases.

Build intelligent workflows covering data ingestion retrieval reasoning tool calling inference response.

Engineer and optimize prompts, context strategies, and agent workflows for accuracy, reliability, and performance.

Design custom domain-specific guardrails to handle complex and non-trivial business scenarios.

Build robust LLM evaluations (Evals) to measure quality, accuracy, hallucination, and task performance.

Debug and trace agentic workflows using MLflow, LangSmith, or similar observability/evaluation platforms.

Continuously evaluate emerging AI frameworks and technologies and bring the right ones into production.

Tech Stack We’re Looking For Must Have:

• Python
• LangChain
• LangGraph
• LLMs & Prompt Engineering RAG
• Vector Embeddings & Semantic Search
• Agent Engineering Multi-Agent Orchestration
• Custom Guardrails
• LLM Evals LangSmith / MLflow / Similar AI Observability Tools

• You'll Be a Great Fit If You Have 6 years of experience across Data Science, ML, AI Engineering, or related domains.
• Have actually built and shipped agentic systems, not just experimented with LLM APIs.
• Can think beyond individual models and design end-to-end AI system architectures.
• Understand the trade-offs between accuracy, latency, cost, reliability, and scalability.
• Enjoy solving ambiguous problems and turning them into production-ready AI solutions