Software Developer Engineer - Agenetic AI

Role Overview

Develop and integrate AI-based solutions using LLMs, prompt engineering, fine-tuning and model optimization. Work on end-to-end AI workflows including data preprocessing, training, evaluation, deployment, monitoring & scaling. Build scalable microservices, APIs and automation pipelines around AI/ML systems.

Responsibilities

• Develop and integrate AI-based solutions using LLMs, prompt engineering, fine-tuning and model optimization.
• Work on end-to-end AI workflows — data preprocessing, training, evaluation, deployment, monitoring & scaling.
• Build scalable microservices, APIs and automation pipelines around AI/ML systems.
• Research and experiment with latest GenAI models, frameworks, vector DBs & inference systems.
• Create clean, well-documented code with clear architecture, flowcharts, datasets & experiment tracking.
• Optimize model latency, cost & performance using quantization, batching, caching, etc.
• Implement evaluation frameworks, benchmarking & continuous improvement for AI systems.
• Participate in collaborative discussions, brainstorming & contribute to product vision and roadmap.
• Stay up-to-date with emerging AI technologies; explore new tools, frameworks & research papers.
• Protect confidential data & ensure responsible use, ethical AI and security compliance.
• Contribute to organization goals by completing assigned deliverables on time with quality.

Requirements

• Bachelor’s/Master’s degree in CSE, IT, AI, Data Science or equivalent.
• Good understanding of Machine Learning fundamentals, Python programming & data structures.
• Knowledge of LLMs, NLP techniques, embeddings, vector search systems.
• Familiarity with AI frameworks like PyTorch, TensorFlow, Keras, Transformers (Hugging Face).
• Ability to work with APIs, datasets, pipelines & cloud compute for model training or inference.
• Experience (project/internship) with one or more GenAI tasks: Prompt engineering, Model fine-tuning / RAG, Text, speech, or image generation.
• Good understanding of databases – SQL/NoSQL; familiarity with Vector DBs is a plus.
• Hands-on learning mindset — POC building, AI tool experimentation, hackathon participation.
• Ability to document requirements, model architectures, experiments, and results clearly.

Skills

• Python
• PyTorch
• TensorFlow
• LLMs
• NLP