Translate business requirements into robust, scalable AI solutions using RAG, embeddings, vector search, and fine-tuning.
Design, prototype, and implement LLM-driven applications with multi-step agent workflows and orchestration frameworks (e.g., LangGraph, LangChain, LlamaIndex).
Build and maintain APIs, services, and reusable components in Python to support AI applications.
Deploy and monitor AI models in cloud-native environments (GCP, Azure) leveraging Kubernetes, serverless, and MLOps pipelines.
Continuously evaluate model/system performance and implement improvements.
Architecture & Standards
Contribute to the design of modular and reusable AI architectures across projects.
Establish and follow engineering best practices for GenAI development, testing, deployment, and monitoring.
Support the creation of documentation, templates, and playbooks for consistent solution delivery.
Collaboration & Integration
Partner with cross-functional teams to integrate AI capabilities into enterprise applications.
Work closely with business stakeholders to translate challenges into AI-powered solutions.
Share lessons learned and help drive adoption of AI practices across teams.
Ensure AI applications align with security, compliance, and responsible AI standards.
Minimum Requirements
Bachelors or Masters degree in Computer Science, AI/ML, or related technical field.
5+ years of software development experience, with strong proficiency in Python.
3 - 5+ years hands-on experience building GenAI/LLM-based applications, with proven success from PoC to production deployment.
Proficiency in designing retrieval pipelines (document loaders, chunking strategies, embeddings, vector databases like FAISS, Pinecone, ChromaDB).
Expertise in LLM APIs (OpenAI, Claude, Gemini, etc.), prompt engineering, and fine-tuning.
Experience with cloud platforms (GCP, Azure), containerization (Docker, Kubernetes), and MLOps (CI/CD, monitoring).
Strong understanding of API design, microservices, and enterprise integration patterns.
Familiarity with version control systems (e.g., Git, Azure DevOps).
Demonstrated ability to build and scale AI solutions in production.
Preferred Qualifications
Experience with orchestration frameworks (LangGraph, LangChain, LlamaIndex).
Familiarity with DevOps practices such as IaC (Terraform), YAML pipelines, and automation.
Strong communication skills with ability to collaborate across teams and articulate technical concepts clearly.
Proactive, self-motivated problem solver with a track record of delivering high-value solutions.
Leverage vibe and agentic coding tools such as Cursor, Claude Code, and similar frameworks to accelerate AI solution development and orchestrate multi-agent workflows.
Additional Attributes
Analytical mindset with focus on measurable outcomes.
Collaborative team player who thrives in cross-functional environments.
Curiosity and drive to stay current with emerging GenAI technologies.