GenAI / Agentic AI Automation

TymblHub; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

TymblHub is seeking a senior engineer to own AI solutions end to end inside a client's environment, from vague problems to production-grade deployments. You will scope, architect, build, and ship GenAI and agentic solutions, setting the approach for evaluation and responsible AI.

You will design agentic architectures, RAG pipelines, and integration with enterprise data, while mentoring engineers and driving delivery capabilities across the stack.

Qualifications

• Substantial software engineering experience with strong hands-on GenAI/agentic work in production.
• Deep experience with LLMs on AWS Bedrock and provider APIs, and with agent frameworks.
• Experience designing and deploying enterprise AI applications (agentic workflows, RAG, vector databases, embeddings, semantic search).
• A solid handle on prompt engineering, model evaluation, and responsible AI in production.
• Strong full-stack engineering (Python and TypeScript, APIs) and data engineering fundamentals.
• Ability to navigate ambiguity and drive outcomes at pace.

Responsibilities

• Own AI solutions end to end, from discovery to a production-grade deployment on client infrastructure.
• Design agentic architectures and RAG pipelines that hold up at enterprise scale.
• Define the approach to prompt engineering, model evaluation, and responsible AI.
• Integrate solutions into enterprise systems and both structured and unstructured data.
• Make architecture, technology-selection, and design-review decisions.
• Work across the full stack to ship end to end.
• Mentor engineers and raise delivery capability.
• Codify reusable patterns and share field intelligence with the practice.

Skills

GenAI production

LLMs on AWS Bedrock

Prompt engineering

Full-stack development

Python

TypeScript

Data engineering

Ambiguity handling

Tools

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

Pinecone

pgvector

Weaviate

Qdrant

OpenSearch

OpenTelemetry

FastAPI

Node.js

React

Kafka

Kinesis

Docker

Kubernetes

CI/CD

Terraform

Databricks

Snowflake Cortex

Palantir Foundry

Job description

Job Summary

A senior engineer who owns AI solutions end to end inside a client's environment, from a vague problem to a production-grade deployment. As the technical owner, you will scope, architect, build, and ship GenAI and agentic solutions, and set the approach for evaluation and responsible AI.

Key Responsibilities

• Own AI solutions end to end, from discovery to a production-grade deployment on client infrastructure.
• Design agentic architectures and RAG pipelines that hold up at enterprise scale.
• Define the approach to prompt engineering, model evaluation, and responsible AI.
• Integrate solutions into enterprise systems and both structured and unstructured data.
• Make architecture, technology-selection, and design-review decisions.
• Work across the full stack to ship end to end.
• Mentor engineers and raise delivery capability.
• Codify reusable patterns and share field intelligence with the practice.

Required Qualifications

• Substantial software engineering experience with strong hands-on GenAI/agentic work in production.
• Deep experience with LLMs on AWS Bedrock and provider APIs, and with agent frameworks.
• Experience designing and deploying enterprise AI applications (agentic workflows, RAG, vector databases, embeddings, semantic search).
• A solid handle on prompt engineering, model evaluation, and responsible AI in production.
• Strong full-stack engineering (Python and TypeScript, APIs) and data engineering fundamentals.
• Ability to navigate ambiguity and drive outcomes at pace.

Preferred Qualifications

• AWS AI services (Bedrock, SageMaker).
• Enterprise AI platforms (Palantir Foundry/AIP, Databricks, Snowflake Cortex).
• Production deployment (Docker, Kubernetes, CI/CD, Terraform).
• Experience in regulated industries.
• A track record as an early engineer or technical founder building from zero to production.
• AWS Certified Solutions Architect Professional; an AI/ML certification such as AWS Certified Machine Learning – Specialty.

Technical Skills & Tools

• LLMs & AI: AWS Bedrock, Anthropic Claude, OpenAI; prompt engineering, model evaluation, responsible AI
• Agent frameworks & orchestration: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen; workflow orchestration
• Retrieval & RAG: embeddings, vector databases (Pinecone, pgvector, Weaviate, Qdrant, OpenSearch), semantic search, rerankers
• Evaluation & observability: evaluation harnesses, Langfuse, OpenTelemetry
• Languages: Python, TypeScript / JavaScript
• Backend & APIs: FastAPI, Node.js, microservices, REST, GraphQL
• Frontend: React, TypeScript
• Data: SQL / NoSQL, data pipelines, streaming (Kafka, Kinesis), structured and unstructured data
• Good to have: AWS SageMaker, Docker, Kubernetes, CI/CD, Terraform, Databricks, Snowflake, Palantir Foundry