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We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems.
Job Responsibility
Generative AI Pipeline Development
Data and Document Ingestion
AI Service Integration
Retrieval-Augmented Generation (RAG) Pipelines
LLM Integration and Optimization
Backend Services Ownership
Requirements
Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Machine Learning, or related field
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)
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
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
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
Nice to have
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