AmpsTek - Senior Generative AI Engineer - LLM

Ampstek
Anywhere in India/Multiple Locations
2 - 4 Years

Posted on: 17/05/2025

Job Description

Title : Senior Gen AI Engineer - GenAI / ML (LLM, Python, Lang chain).

Location : Remote, India.

Long-Term Contract.

Job Description :

7 to 12 years of professional experience in building Machine Learning models & systems/ software Engineer background and moved to LLM for last 3-4yrs.

Focus :

Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions.

The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration.

Key Responsibilities :

- Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines.

- Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions.

- Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives.

- Translate business needs into scalable, production-ready AI solutions.

- Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases.

- Contribute to architecture discussions, code reviews, and performance optimization.

Skills Required :

- Proficient in Python, Langchain, and SQL.

- Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows.

- Background in machine learning or software engineering with a focus on system-level thinking.

- Experience working with cloud platforms like AWS, Azure, or GCP.

- Ability to work independently while collaborating effectively across teams.

- Excellent communication and stakeholder management skills.

Preferred Qualifications :

- 1+ years of hands-on experience in LLMs and Generative AI techniques.

- Experience contributing to ML/AI product pipelines or end-to-end deployments.

- Familiarity with MLOps and scalable deployment patterns for AI models.

- Prior exposure to client-facing projects or cross-functional AI teams.

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