The Role
Design, fine-tune, and optimize LLMs (LLaMA, OpenAI GPT) using LoRA and quantization; build RAG pipelines with FAISS and hybrid retrieval; develop autonomous agents with LangChain/ADK; implement ML/DL models in PyTorch and TensorFlow; build scalable APIs (FastAPI/Flask) and deploy on AWS/Azure; add guardrails, monitoring, evaluation, and performance optimization; containerize and manage deployments with Docker and Git.
Summary Generated by Built In
Key Responsibilities:
● Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA, quantization).
● Design and implement end-to-end RAG pipelines with vector search (FAISS, hybrid
retrieval, re-ranking).
● Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK), including tool-calling, memory management, and multi-agent orchestration.
● Develop and optimize ML/DL models using PyTorch and TensorFlow, including
multimodal architectures.
● Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
● Implement guardrails, evaluation metrics, monitoring, and performance optimization for
production AI systems.
● Containerize and manage deployments using Docker and Git.
Required Qualifications:
● Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
● Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
● Experience building and fine-tuning LLMs (e.g., LLaMA, OpenAI GPT) including LoRA
and quantization techniques.
● Strong experience in designing RAG pipelines and implementing vector search (FAISS,
hybrid retrieval).
● Experience building AI agents with tool-calling, memory management, and orchestration
(LangChain/ADK)
● Experience developing APIs using FastAPI or Flask.
● Working knowledge of SQL/MySQL, Redis, and cloud deployment (AWS/Azure).
● Familiarity with Docker, Git, and production deployment practices
Tasks
Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA, quantization).
● Design and implement end-to-end RAG pipelines with vector search (FAISS, hybrid
retrieval, re-ranking).
● Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK), including tool-calling, memory management, and multi-agent orchestration.
● Develop and optimize ML/DL models using PyTorch and TensorFlow, including
multimodal architectures.
● Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
● Implement guardrails, evaluation metrics, monitoring, and performance optimization for
production AI systems.
● Containerize and manage deployments using Docker and Git
Requirements
Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
● Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
● Experience building and fine-tuning LLMs (e.g., LLaMA, OpenAI GPT) including LoRA
and quantization techniques.
● Strong experience in designing RAG pipelines and implementing vector search (FAISS,
hybrid retrieval).
● Experience building AI agents with tool-calling, memory management, and orchestration
(LangChain/ADK)
● Experience developing APIs using FastAPI or Flask.
● Working knowledge of SQL/MySQL, Redis, and cloud deployment (AWS/Azure).
● Familiarity with Docker, Git, and production deployment practices.
● Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA, quantization).
● Design and implement end-to-end RAG pipelines with vector search (FAISS, hybrid
retrieval, re-ranking).
● Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK), including tool-calling, memory management, and multi-agent orchestration.
● Develop and optimize ML/DL models using PyTorch and TensorFlow, including
multimodal architectures.
● Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
● Implement guardrails, evaluation metrics, monitoring, and performance optimization for
production AI systems.
● Containerize and manage deployments using Docker and Git.
Required Qualifications:
● Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
● Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
● Experience building and fine-tuning LLMs (e.g., LLaMA, OpenAI GPT) including LoRA
and quantization techniques.
● Strong experience in designing RAG pipelines and implementing vector search (FAISS,
hybrid retrieval).
● Experience building AI agents with tool-calling, memory management, and orchestration
(LangChain/ADK)
● Experience developing APIs using FastAPI or Flask.
● Working knowledge of SQL/MySQL, Redis, and cloud deployment (AWS/Azure).
● Familiarity with Docker, Git, and production deployment practices
Tasks
Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA, quantization).
● Design and implement end-to-end RAG pipelines with vector search (FAISS, hybrid
retrieval, re-ranking).
● Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK), including tool-calling, memory management, and multi-agent orchestration.
● Develop and optimize ML/DL models using PyTorch and TensorFlow, including
multimodal architectures.
● Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
● Implement guardrails, evaluation metrics, monitoring, and performance optimization for
production AI systems.
● Containerize and manage deployments using Docker and Git
Requirements
Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
● Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
● Experience building and fine-tuning LLMs (e.g., LLaMA, OpenAI GPT) including LoRA
and quantization techniques.
● Strong experience in designing RAG pipelines and implementing vector search (FAISS,
hybrid retrieval).
● Experience building AI agents with tool-calling, memory management, and orchestration
(LangChain/ADK)
● Experience developing APIs using FastAPI or Flask.
● Working knowledge of SQL/MySQL, Redis, and cloud deployment (AWS/Azure).
● Familiarity with Docker, Git, and production deployment practices.
Skills Required
- Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
- Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
- Experience building and fine-tuning LLMs (e.g., LLaMA, OpenAI GPT) including LoRA and quantization techniques.
- Strong experience in designing RAG pipelines and implementing vector search (FAISS, hybrid retrieval).
- Experience building AI agents with tool-calling, memory management, and orchestration (LangChain/ADK).
- Experience developing APIs using FastAPI or Flask.
- Working knowledge of SQL/MySQL, Redis, and cloud deployment (AWS/Azure).
- Familiarity with Docker, Git, and production deployment practices.
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The Company
What We Do
Selectify Analytics is an AI-powered data solutions provider and certified AWS Partner that empowers businesses to unlock the value of their data. They provide end-to-end services in Data Engineering, AI/ML, Generative AI, BI & Reporting, and DevOps, transforming raw organizational data into actionable insights. Their mission is to help organizations of all sizes harness data and AI to drive innovation, optimize operations, and achieve sustainable growth.
