AI ML Engineer

JOB DESCRIPTION AI/ML Engineer (2-4 years)

Job location Bangalore/Coimbatore Work from Office 5days

Mumbai/Delhi At client's place (Depends on client's work mode)

What is the role about

We are seeking a passionate and skilled Senior GenAI Engineer to join our GenAI organization and contribute to ShellKode's next-generation AI initiatives.

This role focuses on building scalable GenAI and Agentic AI solutions using AWS cloud-native services, RAG architectures, and enterprise-grade orchestration workflows. The ideal candidate will have hands-on experience with LLM-based development, agentic frameworks, and AWS Bedrock.

What you will do

Design & Development

Design and develop advanced GenAI solutions including:

Retrieval-Augmented Generation (RAG)

Agentic AI workflows (single-agent and multi-agent systems)

Tool-calling and agent-to-agent orchestration

Text-to-SQL, IDP (Intelligent Document Processing)

Summarization, text generation, and multimodal use cases

Build scalable GenAI services using:

Amazon Bedrock (mandatory)

LangChain, LangGraph, AgentCore

Hugging Face APIs

AWS cloud services (Lambda, API Gateway, S3, DynamoDB, Step Functions)

Engineering & Implementation

Develop backend services and pipelines using Python (FastAPI/Flask).

Implement embeddings, retrieval pipelines, chunking strategies, and grounding logic.

Optimize RAG workflows, tool-calling patterns, and agentic reasoning.

Deploy solutions using AWS-native tooling (SageMaker optional but not required for fine-tuning).

Execution & Collaboration

Work closely with technical leads to execute assigned tasks and deliver project modules.

Participate in requirement discussions, POCs, SOW execution, testing, and production rollout.

Collaborate with cross-functional teams, including product owners, architects, and customer teams.

Quality, Performance & Innovation

Monitor and improve solution robustness, latency, accuracy, and scalability.

Implement guardrails, enterprise safety practices, and hallucination mitigation patterns.

Stay updated on emerging LLM models, Bedrock capabilities, agent frameworks, and industry best practices.

What you will need to have

Mandatory Experience

2 to 4 years of overall engineering experience.

Minimum 1 year of hands-on GenAI project experience.

Mandatory Agentic AI experience, including:

Multi-agent orchestration

Tool-calling workflows

Agent reasoning and state management

Strong hands-on experience with AWS Cloud, including:

Amazon Bedrock

Lambda, API Gateway, S3, DynamoDB

Step Functions / Event-driven architectures (preferred)

Practical experience with:

LangChain, LangGraph, AgentCore

Vector databases (Pinecone, Weaviate, Chroma, Milvus)

Technical Skills

Strong Python development experience.

Knowledge of LLM APIs, embeddings, vector search, and RAG workflows.

Experience building scalable backend systems and microservices.

Familiarity with Docker, Git, CI/CD, and cloud-native deployment.

Soft Skills

Strong analytical and problem-solving ability.

Ability to work effectively with cross-functional teams.

Good communication skills for customer and internal interactions.

Other Requirements

Experience in a fast-paced startup or consulting environment is desirable.

Prior customer-facing experience is an advantage.

Candidates must be open to working from customer locations (onsite) when required.

Willingness to travel occasionally for project needs