Artificial Intelligence Engineer

2 - 5 years

4 - 7 Lacs

Posted:18 hours ago| Platform: Naukri logo

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Work Mode

Hybrid

Job Type

Full Time

Job Description

Job Title: Senior Associate Product Engineer AI Engineer

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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)

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