AI Engineer (LLMs, Agentic Systems & Model Training)

Job Title: AI Engineer (LLMs, Agentic Systems & Model Training)

Location:

Employment Type:

Experience Level:

About the Role

AI Engineer

Key Responsibilities

LLM Development & Model Training

• 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.

Collaboration & Research

• 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