Artificial Intelligence Engineer - Python Programming

Key Responsibilities :

- Design and implement end-to-end AI systems including RAG pipelines, vector search infrastructure, and LLM-powered applications for enterprise clients.

- Champion vibe coding practices by leveraging AI-assisted development tools (Cursor, GitHub Copilot, Replit, etc.) to accelerate delivery without compromising code quality.

- Lead the fine-tuning, evaluation, and deployment of large language models (e.g., GPT, Claude, Llama, Mistral) tailored to client-specific use cases.

- Build and maintain prompt engineering frameworks, evaluation harnesses, and guardrail systems for production LLM deployments.

- Architect scalable vector database solutions using tools such as Pinecone, Weaviate, Qdrant, or pgvector to power semantic search and knowledge retrieval systems.

- Collaborate with cross-functional consulting teams to translate business requirements into technical AI architectures and working prototypes.

- Conduct code reviews, enforce engineering best practices, and contribute to the internal AI engineering playbook.

- Stay current with rapidly evolving AI research and tooling; evaluate and integrate new techniques and libraries as appropriate.

- Mentor junior AI engineers and contribute to a culture of continuous learning and knowledge sharing.

Required Qualifications :

- 10 -12 years of professional software engineering experience, with a significant focus on AI/ML systems over at least the last 3 years.

- Hands-on experience with AI-assisted development tools (Cursor, GitHub Copilot, Replit, Amazon CodeWhisperer, or equivalent) - you do not just use them, you master them.

- Deep expertise in large language models : prompt engineering, evaluation, fine-tuning, and API integration (OpenAI, Anthropic, Hugging Face, Cohere, etc.).

- Strong experience with Retrieval-Augmented Generation (RAG) architectures, embedding models, and vector databases (Pinecone, Weaviate, Qdrant, Chroma, pgvector).

- Proficiency in Python for ML/AI engineering; familiarity with frameworks such as LangChain, LlamaIndex, Haystack, or similar orchestration libraries.

- Experience with ML fine-tuning workflows including data preparation, PEFT/LoRA, RLHF, and model evaluation pipelines.

- Solid understanding of cloud AI services and deployment (AWS SageMaker, Azure AI, Google Vertex AI, or equivalent).

- Excellent communication skills - able to explain complex AI systems to technical and non-technical stakeholders alike.

Nice To Have :

- Experience working in a consulting or client-services environment.

- Familiarity with agentic AI frameworks (AutoGen, LangGraph) and multi-agent orchestration patterns.

- Prior work with multimodal models (vision + language) or speech AI systems.

- Contributions to open-source AI projects or published research.

- Knowledge of MLOps tools and practices (MLflow, Weights & Biases, DVC, Kubeflow).

- Exposure to edge AI deployment or on-premise LLM hosting (Ollama, vLLM, TGI).

What We Offer :

- Competitive compensation package benchmarked against global technology market standards.

- Exposure to cutting-edge AI projects across diverse industries and geographies.

- A culture that genuinely embraces vibe coding and AI-native workflows - we dogfood what we preach.

- Access to a curated library of AI tools, compute resources, and research subscriptions.

- Structured mentorship, learning stipends, and conference attendance support.

- A collaborative, inclusive, and intellectually stimulating team environment.

- Opportunity to shape the AI engineering practice of a fast-growing firm.