Senior AI Engineer

Job Title: Senior AI Engineer
Location: - Viman Nagar, Pune
Experience- 6+ Years
Timings: -11 am to 8 pm
Mode:- Hybrid Model

Be part of the AcquireX team that unleashes the power of leading-edge technologies to help improve e-commerce processes in the hardware world.

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Key Responsibilities

Work on the Full GenAI Stack: Build and optimize full-stack applications using spec- driven development, Next.js, TypeScript, Python, Node.js, .NET, and managed services such as Azure OpenAI.

Develop AI Systems: Design, develop, and deploy sophisticated AI systems, including Retrieval-Augmented Generation (RAG), to power next-generation search and discovery experiences.

Develop and Fine-Tune LLMs: Build and optimize generative AI models for automated content creation, summarization, and metadata enrichment.

Implement LLMOps: Establish robust observability using tools such as OpenTelemetry and Prometheus, tracking latency, cost, accuracy, hallucinations, toxicity, and data drift.

Backend Development: Develop scalable backend services, REST APIs, and integrations using Python, Node.js, and .NET to support AI-powered applications.

Database Management: Design, integrate, and optimize databases for efficient data storage, retrieval, and processing across AI and full-stack applications.

Must-Have Skills

Programming Languages: Strong proficiency in Python for AI/ML development, LLM integration, automation, and backend development.

Backend Technologies: Experience with Node.js and .NET for developing scalable backend services, REST APIs, and enterprise application integrations.

Full-Stack Development: Hands-on experience with Next.js, TypeScript, and modern web application development.

Production LLM Experience: Experience in full-stack development with demonstrable success in productionizing LLM applications using modern frameworks like DSPY, LangChain, LlamaIndex, or Hugging Face Transformers.

RAG Expertise: Deep, practical knowledge of RAG architecture, including advanced prompt engineering, chunking strategies, and proficiency with vector databases (e.g., Pinecone, Weaviate, Milvus).

Cloud Proficiency: Expertise with managed LLM services (Azure OpenAI Service or AWS Bedrock). Strong foundational cloud skills in either Azure or AWS for compute orchestration (AKS/EKS), serverless functions, and storage.

MLOps Acumen: Solid experience with Docker, CI/CD pipelines (e.g., GitHub Actions, Argo), and model registries.

Nice-to-Have Skills

Experience with agentic workflows (e.g., AutoGen, CrewAI).

Familiarity with multi-modal models (text, image, etc.).

Knowledge of advanced LLM fine-tuning techniques (e.g., LoRA, QLoRA).

Strong SQL skills (especially with ClickHouse) and a keen eye for inference cost optimization.