AI Engineer

Overview:

We are looking for an experienced AI Engineer to design, build, and deploy production-grade AI solutions. You will work across the full lifecycle of Generative AI and Agentic AI applications, from prototyping and model selection to deployment, evaluation, and continuous improvement.

The ideal candidate has strong hands-on Python expertise, a solid grasp of modern LLM ecosystems, and the ability to take ownership of AI-driven solutions with minimal supervision.

Key Responsibilities:

AI Solution Development

• Design, develop, and maintain scalable AI/ML applications using Python.
• Build Generative AI and LLM-powered applications using leading models (such as OpenAI GPT, Anthropic Claude, Google Gemini) and open-source models (such as Llama, Mistral, Qwen).
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, hybrid search, and re-ranking.
• Apply advanced prompt engineering, structured outputs, and function/tool calling techniques.

Agentic AI and Integrations

• Design and build AI agents and multi-agent workflows with memory, planning, tool use, and human-in-the-loop controls.
• Develop tool integrations using APIs and the Model Context Protocol (MCP).
• Build and expose AI services through RESTful APIs (FastAPI or similar) and integrate them with applications, databases, and third-party systems.

Deployment and Operations (LLMOps)

• Deploy and manage AI solutions on cloud platforms (AWS, Azure, or GCP) using services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
• Containerize applications with Docker and contribute to CI/CD pipelines for reliable releases.
• Implement observability, logging, and tracing for AI applications in production.
• Optimize solutions for latency, cost (token usage, caching, model routing), and scalability.

Quality, Safety, and Evaluation

• Define and run evaluation frameworks to measure accuracy, relevance, hallucination rate, and overall response quality.
• Implement guardrails for safety, data privacy, and protection against prompt injection and misuse.
• Continuously test, monitor, and improve AI systems based on metrics and user feedback.

Collaboration and Ownership

• Translate business and technical requirements into well-architected AI solutions independently.
• Collaborate with product, engineering, and business teams to deliver projects on time.
• Contribute to solution architecture, code reviews, testing, and technical documentation.
• Stay current with advancements in AI and proactively recommend relevant tools and approaches.

Required Skills:

• 3+ years of hands-on experience in Python development, with strong knowledge of OOP, async programming, and writing clean, maintainable code.
• Solid understanding of AI/ML fundamentals and their practical application.
• Proven experience building Generative AI and LLM-based applications.
• Hands-on experience with Agentic AI concepts and at least one agent framework (such as LangGraph, CrewAI, AutoGen, LlamaIndex, OpenAI Agents SDK, or similar).
• Practical experience with RAG, embeddings, and vector databases (such as Pinecone, Qdrant, Weaviate, Milvus, ChromaDB, or pgvector).
• Experience building and consuming REST APIs and integrating AI into existing systems.
• Working experience with at least one cloud platform: AWS, Azure, or GCP.
• Proficiency with Git and collaborative development workflows.
• Strong analytical, problem-solving, and debugging skills.
• Good communication skills, with the ability to understand requirements and explain technical decisions clearly.

Good to Have:

• Experience deploying and maintaining AI applications in production environments.
• Familiarity with LLM evaluation and observability tools (such as LangSmith, Langfuse, RAGAS, DeepEval, or Arize Phoenix).
• Experience with fine-tuning techniques (LoRA, QLoRA, PEFT) or model serving tools (vLLM, Ollama, Hugging Face).
• Knowledge of Docker, Kubernetes, and CI/CD tools (GitHub Actions, GitLab CI, Jenkins).
• Experience with SQL and NoSQL databases (PostgreSQL, MongoDB, Redis).
• Exposure to multimodal AI (vision, speech, document understanding).
• Understanding of AI security, Responsible AI principles, and data privacy practices.
• Relevant certifications (AWS, Azure AI Engineer, Google Cloud ML) or contributions to open-source AI projects.

Educational Qualifications:

• Bachelors degree in Computer Science, Information Technology, AI/Data Science, or a related field (or equivalent practical experience).

What Were Looking For:

• Depth over breadth: strong, hands-on technical knowledge and the ability to independently own solutions end to end.
• Candidates who can clearly walk through projects they have built, including the architecture, technologies used, key technical decisions, challenges faced, and the outcomes achieved.
• A builder mindset with curiosity, accountability, and a commitment to continuous learning in a fast-evolving field.