About the Role

Responsibilities

Generative AI Pipeline Development

• • Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads.
• • Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs.
• • Build CI/CD pipelines with integrated prompt regression testing and version control.
• • Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows.
• • Monitor system performance using tools like Langfuse or Prometheus.

Data and Document Ingestion

• • Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data.
• • Apply preprocessing pipelines for text, images, and code.
• • Ensure data integrity, format consistency, and security across sources.

AI Service Integration

• • Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.).
• • Build internal APIs for smooth backend-AI communication.
• • Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets.
• • Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy.

Retrieval-Augmented Generation (RAG) Pipelines

• • Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API).
• • Design custom retrieval strategies for multi-modal or multi-source documents.
• • Apply post-retrieval ranking using DPO or feedback-based techniques.
• • Improve contextual relevance through re-ranking, chunk merging, and scoring logic.

LLM Integration and Optimization

• • Manage prompt engineering, model interaction, and tuning workflows.
• • Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design.
• • Optimize generation using temperature tuning, token limits, and speculative decoding.
• • Integrate observability and cost-monitoring into LLM workflows.

Backend Services Ownership

• • Design and maintain scalable backend services supporting GenAI applications.
• • Implement monitoring, logging, and performance tracing.
• • Build RBAC (Role-Based Access Control) and multi-tenant personalization.
• • Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production.

Required Skills and Qualifications

Education

Experience

• • 5+ years of experience in AI/ML engineering with end-to-end pipeline development.
• • Hands-on experience building and deploying LLM/RAG systems in production.
• • Strong experience with public cloud platforms (AWS, Azure, or GCP).

Technical Skills

• • Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch.
• • Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph.
• • Experience with RESTful API development and version control using Git.
• • Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval.
• • Familiarity with Docker, Kubernetes, and scalable microservice design.
• • Experience with observability tools like Prometheus, Grafana, or Langfuse.

Generative AI Specific Skills

• • Knowledge of LLMs, VAEs, Diffusion Models, GANs.
• • Experience building structured + unstructured RAG pipelines.
• • Prompt engineering with safety controls, schema enforcement, and hallucination mitigation.
• • Experience with prompt testing, caching strategies, output filtering, and fallback logic.
• • Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods.

Soft Skills

• • Strong analytical, problem-solving, and debugging skills.
• • Excellent collaboration with cross-functional teams: product, QA, and DevOps.
• • Ability to work in fast-paced, agile environments and deliver production-grade solutions.
• • Clear communication and strong documentation practices.

Preferred Qualifications

• • Experience with OCR, document parsing, and layout-aware chunking.
• • Hands-on with MLOps and LLMOps tools for Generative AI.
• • Contributions to open-source GenAI or AI infrastructure projects.
• • Knowledge of GenAI governance, ethical deployment, and usage controls.
• • Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI.

Experience and Shift