Artificial Intelligence Engineer

Job Title: Senior Associate Product Engineer AI Engineer

Location:

Experience:

Employment Type:

Job Summary:

We are seeking an AI Engineer with strong proficiency in Python and hands-on experience in building AI-powered applications. The ideal candidate should have experience working with FastAPI, Langchain, and Pydantic, along with a solid understanding of Generative AI concepts, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG).

Key Responsibilities:

• Develop, integrate, and optimize AI-powered applications using FastAPI and Langchain.
• Design and deploy APIs using FastAPI with Pydantic for data validation.
• Implement and fine-tune solutions leveraging Large Language Models (LLMs).
• Build and optimize Prompt Engineering pipelines for LLM interactions.
• Design and implement Retrieval-Augmented Generation (RAG) solutions for enhanced contextual responses.
• Generate embeddings and implement similarity search pipelines for vector retrieval systems.
• Collaborate with data scientists, product managers, and engineers to deliver high-quality AI applications.
• Continuously research and experiment with advancements in Generative AI technologies.
• Monitor, test, and optimize AI applications for performance, accuracy, and scalability.

Key Skills & Technologies:

• Programming: Python (Intermediate to Advanced)
• Frameworks & Libraries:
• FastAPI (API development)
• Langchain (LLM integrations & workflows)
• Pydantic (Data validation and settings management)
• Pandas, NumPy (Data processing)
• AI/ML Concepts:
• Generative AI (Foundational to Intermediate)
• Large Language Models (LLM) – Understanding architecture, capabilities, and limitations
• Fine-tuning LLMs (Hugging Face Transformers, OpenAI APIs)
• Prompt Engineering – Techniques for effective LLM interactions
• Retrieval-Augmented Generation (RAG) – Building hybrid retrieval-generation systems
• Evaluation metrics for LLMs (perplexity, BLEU, ROUGE, retrieval accuracy)
• Data & Databases:
• SQL / NoSQL (PostgreSQL, MongoDB)
• Vector Databases (FAISS, Chroma, Milvus, Weaviate)
• Deployment & DevOps:
• Git, GitHub/GitLab (Version Control)
• Docker & Kubernetes (Containerization & orchestration)
• CI/CD pipelines (GitHub Actions, Jenkins, MLflow pipelines)
• Cloud AI Platforms (AWS SageMaker, Azure ML, GCP Vertex AI)

Preferred/Optional (Good to Have):

• Experience with AI Agent Frameworks such as:
• LangGraph
• CrewAI
• Autogen
• Knowledge of Vector Databases (e.g., FAISS, Chroma)
• Multi-step reasoning and AI agent pipelines
• Cloud Platforms (AWS, Azure, GCP)
• Containerization (Docker)
• Monitoring & observability for AI applications (Prometheus, Grafana, OpenTelemetry)
• Unit testing for AI workflows (pytest, hypothesis)