Senior Engineer Software

Role: Applied GenAI Research Engineer

Experience: 6-9 years
Employment Type: Full-time

Role Overview:

We are looking for a Generative AI Engineer to design, build, and deploy AI-powered solutions using foundation models across text, image, and video. This role focuses on applying Generative AI techniques to real-world problems, building reliable and scalable systems, and rapidly iterating from ideas to production-ready solutions.

Key Responsibilities:

• Design, build, and iterate GenAI-powered applications, prototypes, and MVPs
• Own the architecture and technical direction of GenAI solutions
• Develop Agentic AI systems capable of planning, reasoning, decision-making, and taking autonomous actions , including but not limited to tool invocation, workflow orchestration, and interaction with external systems and APIs
• Build and optimize advanced multimodal RAG architectures across text, documents, images, and video metadata, leveraging hybrid retrieval (vector + keyword), self-querying and query decomposition, reranking, context compression, and multi-hop retrieval and reasoning

Fine-tune and adapt foundation models, including:

• Large Language Models (LLMs)
• Large Vision Models (LVMs) and multimodal models
• Design and manage memory and context systems for conversational and agent-based AI
• Apply context engineering techniques to dynamically construct, optimize, and manage model context, including prompt structuring, context window optimization, relevance filtering, summarization, and grounding across multimodal inputs
• Apply prompting, evaluation, and guardrails across modalities
• Integrate GenAI capabilities into applications via APIs and services
• Evaluate models for quality, latency, cost, robustness, and safety
• Collaborate with product, data, and platform teams
• Stay current with advances in Generative AI and multimodal research

Required Skills:

Generative AI & Machine Learning:

• Hands-on experience with foundation models (text, image, or multimodal)
• Experience with LangChain, LangGraph, n8n, or similar agent frameworks
• Strong experience building RAG-based systems
• Experience with vector databases
• Practical experience with model fine-tuning (LoRA/QLoRA, PEFT, adapters)
• Experience designing agentic AI workflows
• Knowledge of memory and context management for GenAI systems
• Experience working with multiple models (OpenAI, Anthropic, Gemini, open-source)
• Experience implementingprompting strategies, evaluation frameworks, and guardrails
• Ability to evaluate models forquality, latency, cost, robustness, and safety

Software Engineering:

• Strong Python programming skills
• Solid experience with Git and collaborative development workflows
• Experience building and consuming APIs
• CI/CD pipelines for AI or ML applications
• Experience with cloud platforms (GCP, or Azure preferred)
• Strong debugging, testing, and performance optimization skills

Nice to Have:

• Experience with containerization and microservices (Docker, Kubernetes)
• Familiarity with MLOps / LLMOps / ModelOps
• Experience deploying GenAI systems to production
• Contributions to open-source AI/ML projects
• Experience in fast-paced innovation environments

What Were Looking For:

• Strong builder mindset with rapid experimentation skills
• Comfort working with ambiguous and evolving problem statements
• Ability to balance research exploration with engineering rigor