Senior LLM Engineer Guru Kripa Consultants

  • company name Guru Kripa Consultants
  • working location Office Location
  • job type Full Time

Experience: 8 - 8 years required

Pay: INR 2800000 - INR 4000000 /year

Type: Full Time

Location: Bangalore

Skills: SQL, Python, RAG, Langchain

About Guru Kripa Consultants

Job Description

Senior Gen AI Engineer

Chennai / Pune / Bangalore / Noida / Gurgaon- WFO, 3 days hybrid

35 - 40 LPA

Senior Gen AI Engineer GenAI / ML (Python, Langchain)
Full Time
Location: India
Overall Experience: 712 Years
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.