Job Description:
We are seeking a highly skilled AI Engineer with 4–7 years of software engineering experience and deep expertise in Generative AI, Agentic AI, and AI platform development. The ideal candidate will have a strong engineering foundation, hands-on experience building production-grade AI systems, and the ability to leverage modern AI tools to accelerate software development while maintaining high-quality standards.
Key Responsibilities
• Design, develop, and deploy enterprise-scale Generative AI solutions using LLMs, SLMs, and multimodal models.
• Build advanced Retrieval-Augmented Generation (RAG) systems, including hybrid search, re-ranking, context management, and knowledge-grounded generation.
• Develop Agentic AI solutions capable of planning, reasoning, tool usage, workflow orchestration, and multi-agent collaboration.
• Design and implement MCP (Model Context Protocol) based architectures, including MCP servers, tools, and enterprise integrations.
• Fine-tune, evaluate, and optimize foundation models and SLMs for domain-specific use cases.
• Build scalable, secure, and production-ready AI applications following software engineering best practices.
• Implement LLMOps/MLOps practices including evaluation, observability, monitoring, governance, prompt management, and model lifecycle management.
• Deploy AI solutions on AWS, Azure, or GCP using modern cloud-native architectures.
• Collaborate with business stakeholders, architects, and global teams to translate business challenges into AI-driven solutions.
• Stay current with emerging AI technologies, frameworks, and research.
Required Skills & Experience
• 5–7 years of software engineering experience with at least 3+ years focused on AI/ML or Generative AI.
• Strong programming skills in Python with expertise in building scalable backend services and APIs.
Hands-on experience with:
• RAG architectures and vector databases
• Agentic AI frameworks and multi-agent systems
• MCP (Model Context Protocol) implementations
• Prompt engineering, evaluation frameworks, and AI guardrails
• LLMs such as OpenAI, Claude, Gemini, Llama, Mistral, or similar
• Experience with frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or PydanticAI.
• Strong understanding of data structures, algorithms, system design, distributed systems, and software architecture.
• Experience with Docker, Kubernetes, CI/CD, and cloud platforms (AWS, Azure, or GCP).
• Familiarity with LLMOps/MLOps, observability, and evaluation tools such as LangSmith, MLflow, Promptfoo, DeepEval, Ragas, or similar.
AI-Assisted Engineering (Must Have)
• Demonstrated ability to effectively use AI-powered development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, Cline, Aider, Codex, or similar.
• Experience building AI-native development workflows for code generation, debugging, testing, refactoring, documentation, and productivity enhancement.
• Exposure to engineering platforms and CI/CD tools such as Harness, GitHub Actions, Azure DevOps, Jenkins, or GitLab CI/CD.
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, AI/ML, or related quantitative disciplines.