AI Engineer (LLMs, Agentic Systems & Model Training)

Kayana | Ordering & Payment Solutions; Mumbai; On-site; INR 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Competitive salary and benefits

Opportunity to work with cutting-edge AI systems

Collaborative environment

Career growth opportunities

Job summary

A tech company specializing in AI solutions is seeking an AI Engineer with expertise in Large Language Models and AI Agents. The role involves optimizing models, deploying AI agents, and developing scalable AI services using Python and Java. Ideal candidates should have a Bachelor's or Master's in a related field, along with strong programming skills and experience in fine-tuning LLMs and architecting agentic systems. This position offers competitive salary and career growth opportunities in a collaborative work environment.

Qualifications

• Hands-on experience training and optimizing LLMs.
• Deep understanding of transformer architectures.
• Experience with agent's frameworks (LangChain, AutoGen).

Responsibilities

• Fine-tune, train, and optimize LLMs for specific business use cases.
• Build and deploy AI agents capable of multi-step reasoning.
• Develop scalable AI services using Python and Java.

Skills

Large Language Models (LLMs)

Python

Java

AI Agents

Vector Embeddings

Transformer Architectures

RAG Methodologies

Distributed Training

Education

Bachelor’s/Master’s in computer science, AI, Machine Learning, or related field

Tools

TensorFlow

PyTorch

Docker

Kubernetes

AWS

Azure

GCP

Job description

AI Engineer (LLMs, Agentic Systems & Model Training)

Employment Type: Full-Time

Experience Level: Mid–Senior

About the Role

We are seeking a highly skilled AI Engineer with deep expertise in Large Language Models (LLMs), AI Agents, and advanced retrieval and fine-tuning techniques. The ideal candidate has hands‑on experience training and optimizing LLMs, building agentic workflows, utilizing vector embeddings, and implementing Agentic RAG and Cache‑RAG architectures. Strong proficiency in Python and Java is required.

Key Responsibilities

• Fine‑tune, train, and optimize LLMs (open‑source or proprietary) for specific business use cases.
• Implement supervised fine‑tuning (SFT), RLHF, PEFT/LoRa, and other parameter‑efficient training methods.
• Evaluate and improve model performance using modern benchmarking and evaluation tools.

AI Agents & Autonomous Workflows

• Build and deploy AI agents capable of tool use, planning, memory, and multi‑step reasoning.
• Architect agentic systems that interact with external APIs, internal tools, and knowledge sources.
• Optimize agent reliability, latency, and cost using best practices.

RAG & Vector Embeddings

• Design and implement Agentic RAG, Cache‑RAG, and hybrid retrieval pipelines.
• Work with vector databases (Postgres Vector, Pinecone, FAISS, Milvus, Chroma, Weaviate, etc.).
• Generate and manage embeddings for semantic search, retrieval‑augmented generation, and caching.
• Ensure integrity, quality, and relevance of retrieval datasets.

Software Engineering

• Develop scalable AI services using Python and Java.
• Build APIs, microservices, and data pipelines that support AI workflows.
• Write efficient, production‑ready, clean, and well‑documented code.
• Partner with data scientists, ML engineers, product teams, and researchers.
• Stay current with state‑of‑the‑art LLM research, agent frameworks, and vector search technologies.
• Propose and prototype innovative AI features and architectures.

Required Skills & Qualifications

• Bachelor’s/Master’s in computer science, AI, Machine Learning, or related field.
• Strong proficiency in Python and Java, with demonstrable project experience.
• Hands‑on experience fine‑tuning and training LLMs (e.g., Llama, Mistral, GPT variants, Qwen, Gemma).
• Deep understanding of transformer architectures, tokenization, and inference optimization.
• Experience with agent's frameworks (LangChain, AutoGen, OpenAI Agents, LlamaIndex agents, custom agents).
• Practical knowledge of vector embeddings, ANN search, and RAG methodologies.
• Familiarity with GPU pipelines, distributed training, and model deployment.
• Understanding of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).

Preferred Qualifications

• Experience with multi‑modal LLMs (vision, audio, code).
• Knowledge of model quantization (GPTQ, AWQ) and inference acceleration.
• Experience with orchestration tools (Ray, Prefect, Airflow).
• Contributions to open‑source AI projects.

What We Offer

• Competitive salary and benefits
• Opportunity to work with cutting‑edge AI systems
• A collaborative environment that encourages innovation
• Career growth and leadership opportunities