Sr AI Engineer

SecPod Technologies, Inc.; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

SecPod Technologies, Inc. in Bengaluru is seeking a Sr. AI/ML Engineer to drive generative AI initiatives, focusing on LLMs and advanced models for vulnerability and endpoint management.

You will collaborate with software engineers to deploy GenAI solutions via APIs, fine-tune models, build RAG pipelines, and stay current with GenAI research.

Qualifications

• Bachelor's or Master's in CS, DS, AI or related quantitative field.
• Strong Python plus Pandas/NumPy/Scikit-learn experience.
• Solid ML/DL fundamentals and Transformer knowledge.
• Basic NLP concepts and text preprocessing.
• Curious, analytical approach to problems.

Responsibilities

• Develop, fine-tune, and evaluate generative models (LLMs) on domain data.
• Design AI agents for vulnerability and endpoint management with data integration.
• Engineer prompts for chatbots, summarization, and content generation.
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines.
• Collaborate with software engineers to deploy GenAI via APIs.
• Stay current with GenAI research and experiment with new tools and architectures.

Skills

Python

NumPy

Pandas

Scikit-learn

Transformer

NLP basics

Problem solving

Education

Bachelor's degree

Master's degree

Tools

PyTorch

TensorFlow

LangChain

Hugging Face Transformers

LlamaIndex

Git

Job description

We are looking for a creative and driven Sr. AI/ML Engineer to join our forward-thinking AI team. This is an exciting opportunity to work at the cutting edge of artificial intelligence, focusing on the development and implementation of Generative AI solutions. You will contribute to projects that leverage Large Language Models (LLMs) and other generative techniques to solve complex business challenges and create innovative products. If you are passionate about building the next generation of intelligent systems, this role is for you!

What You'll Do

• Model Development & Fine-Tuning: Assist in the training, fine-tuning, and evaluation of state-of-the-art generative models (like LLMs) on domain-specific datasets.
• Agentic AI: Design and develop intelligent AI agents for vulnerability and endpoint management, capable of integrating with databases and servers to collect data, perform analysis, and generate actionable remediation recommendations.
• Prompt Engineering: Design, iterate, and refine prompts to effectively steer model behavior and ensure high-quality, relevant outputs for applications like chatbots, summarization, and content creation.
• RAG Systems: Help build and maintain Retrieval-Augmented Generation (RAG) pipelines to ground models in factual, up-to-date information from external data sources.
• Data Management: Collect, clean, and preprocess large volumes of structured and unstructured data to prepare it for model training and analysis.
• Collaboration & Integration: Work closely with software engineers to integrate GenAI models into user-facing applications via APIs.
• Research & Experimentation: Stay current with the latest research in GenAI and conduct experiments to explore new tools, techniques, and model architectures.

What We're Looking For

Required Qualifications:

• Educational Background: A Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Statistics, or a related quantitative field.
• Programming Skills: Strong proficiency in Python and familiarity with core data science libraries such as Pandas, NumPy, and Scikit-learn.
• ML/DL Fundamentals: A solid understanding of fundamental machine learning concepts and deep learning principles, especially neural networks and the Transformer architecture.
• NLP Foundations: Basic knowledge of Natural Language Processing (NLP) concepts like text preprocessing, word embeddings, and sequence modeling.
• Problem-Solving Mindset: A curious and analytical approach to solving complex problems.

Preferred Qualifications:

• Hands-on LLM Experience: Practical experience (through projects, internships, or coursework) with LLMs like GPT, Llama, Gemini, or other open-source models.
• AI Frameworks: Familiarity with deep learning frameworks like Langchain, Langraph PyTorch or TensorFlow.
• GenAI Ecosystem: Exposure to libraries like Hugging Face Transformers, LangChain, or LlamaIndex.
• Cloud Platforms: Basic understanding of a cloud environment (AWS, GCP, or Azure).
• Version Control: Experience using Git for collaborative code development.