Gen AI Developer

Role Summary

We are seeking a highly skilled and visionary Technical Project Lead / Architect / GenAI Lead to spearhead next-generation AI initiatives. This role demands deep expertise in Large Language Models (LLMs), cloud-native architecture, and strong technical leadership to deliver scalable, secure, and high-performance GenAI solutions primarily on AWS cloud platforms.

Key Responsibilities

Lead the design, development, and deployment of GenAI solutions leveraging Large Language Models (LLMs) on AWS cloud platforms.

Architect scalable, event-driven cloud-native solutions using AWS Lambda, SNS, SQS, Step Functions, and related services.

Drive prompt engineering strategies including prompt versioning, optimization, and chain-of-thought design.

Oversee fine-tuning, embeddings, and conversational flow development for intelligent, context-aware AI applications.

Integrate and manage LLM APIs including Azure OpenAI and other emerging AI models.

Utilize vector databases such as ElasticSearch or OpenSearch for Retrieval-Augmented Generation (RAG).

Lead model evaluation activities including output analysis, performance tuning, and quality benchmarking.

Collaborate with data scientists, engineers, and product managers to align AI initiatives with business goals.

Mentor engineering teams and enforce best practices in GenAI development and cloud architecture.

Stay current with advancements in AI, cloud services, and LLM frameworks to continuously improve solutions.

Required Skills & Experience

6+ years of experience in AWS cloud computing and architecture.

4+ years of hands-on experience with Generative AI and Large Language Models.

Strong programming expertise in Python with frameworks such as FastAPI or Flask.

Proven experience integrating LLMs into real-world, production-grade applications.

Deep expertise with AWS services including Lambda, SNS/SQS, Step Functions, and event-driven patterns.

Mandatory experience with Azure OpenAI services; knowledge of LLaMA and other models is a plus.

Hands-on experience with vector databases like ElasticSearch, OpenSearch, or similar.

Advanced proficiency in prompt engineering, prompt optimization, and chain-of-thought techniques.

Experience using LLM frameworks such as LangChain and LlamaIndex.

Strong understanding of Retrieval-Augmented Generation (RAG) architectures.

Expertise in model evaluation, performance tuning, and output quality assessment.

Excellent leadership, communication, and project management skills.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field.