Sr. MLOps Engineer

NexTurn Inc.; Hyderabad; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

NexTurn Inc. in Hyderabad is looking for a talented AI Engineer to build and deploy advanced AI applications including chatbots and decision automation systems. The role involves designing RAG pipelines, implementing multi-agent workflows, and ensuring compliance with regulatory standards. Candidates should possess a Bachelor's degree in a technical field and have extensive knowledge of LLMs, cloud platforms, and AI governance practices. The position offers a dynamic work environment and an opportunity to work on innovative AI technologies.

Qualifications

• Solid understanding of LLMs, transformers, embeddings, and tokenization.
• Experience with RAG systems and retrieval optimization.
• Knowledge of chunking strategies and embedding techniques.
• Hands-on with vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Experience with LangChain / LlamaIndex / LangGraph / similar frameworks.
• Experience evaluating models (precision@k, recall@k, LLM metrics).
• Familiarity with RAG evaluation tools (RAGAS, TruLens, DeepEval).
• Experience with cloud platforms (AWS / GCP / Azure).
• Knowledge of Docker, Kubernetes, CI/CD pipelines.
• Understanding of data versioning, lineage, and governance.
• Experience with monitoring, logging, and observability.
• Exposure to multi-agent systems and orchestration.
• Knowledge of fine-tuning / LoRA.
• Awareness of Responsible AI and compliance practices.

Responsibilities

• Build and deploy AI applications: copilots, chatbots, code assistants, document agents.
• Design and optimize RAG pipelines (chunking, embeddings, retrieval tuning).
• Implement multi-agent systems and orchestration workflows.
• Develop pipelines for prompt data, grounding, and retrieval datasets.
• Build and maintain document loaders and preprocessing pipelines.
• Maintain audit trails for model versions and prompts.
• Support CI/CD pipelines for ML code and infrastructure.
• Ensure ML workflows are reproducible, traceable, and auditable.
• Implement explainability (SHAP, LIME).
• Enforce access controls and data security policies.
• Align with regulatory standards (GDPR, SOC2).
• Manage embedding lifecycle (indexing, re-embedding, updates).
• Track hallucination rates and response quality.

Skills

LLMs understanding

RAG systems

Embedding techniques

Vector databases (FAISS, Pinecone)

LangChain / LlamaIndex frameworks

Model evaluation (precision@k)

RAG evaluation tools

Cloud platforms (AWS, GCP, Azure)

Docker

Kubernetes

CI/CD pipelines

Data versioning and governance

Monitoring and logging

Multi-agent systems

Fine-tuning / LoRA

Responsible AI awareness

Education

Bachelor’s degree in Computer Science

Job description

Qualifications

• Solid understanding of LLMs, transformers, embeddings, and tokenization.
• Experience with RAG systems and retrieval optimization.
• Knowledge of chunking strategies and embedding techniques.
• Hands‑on with vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Experience with LangChain / LlamaIndex / LangGraph / similar frameworks.
• Experience evaluating models (precision@k, recall@k, LLM metrics).
• Familiarity with RAG evaluation tools (RAGAS, TruLens, DeepEval).
• Experience with cloud platforms (AWS / GCP / Azure).
• Knowledge of Docker, Kubernetes, CI/CD pipelines.
• Understanding of data versioning, lineage, and governance.
• Experience with monitoring, logging, and observability.
• Exposure to multi‑agent systems and orchestration.
• Knowledge of fine‑tuning / LoRA.
• Awareness of Responsible AI and compliance practices.

Bachelor’s degree in computer science, Engineering, Information Systems, or related technical field (or equivalent practical experience).

Job Description

• Build and deploy AI applications: copilots, chatbots, code assistants, document agents, and decision automation systems. Design and optimize RAG pipelines (chunking, embeddings, retrieval tuning).
• Implement multi‑agent systems and orchestration workflows.
• Develop pipelines for prompt data, grounding, and retrieval datasets.
• Build and maintain document loaders and preprocessing pipelines.
• Maintain audit trails for model versions, prompts, datasets, embeddings, and outputs.
• Support CI/CD pipelines for ML code, models, and infrastructure (GitHub‑based).
• Ensure ML workflows are:
  • Reproducible.
  • Traceable.
  • Auditable (aligned with automotive engineering expectations).
• Implement explainability (SHAP, LIME, prompt tracing).
• Enforce access controls and data security policies.
• Align with regulatory standards (GDPR, SOC2, Responsible AI).
• Ensure high data quality (cleaning, parsing, enrichment).
• Implement data versioning, lineage, and governance for AI systems.
• Manage embedding lifecycle (indexing, re‑embedding, updates).
• Ensure RAG data freshness via incremental ingestion and re‑indexing.
• Evaluate embedding models and retrieval performance.
• Measure LLM output quality (relevance, hallucination, faithfulness).
• Implement robust observability for AI/ML/LLM systems (latency, drift, reliability, performance).
• Track hallucination rates, response quality, and latency.
• Monitor token usage, cost, and prompt effectiveness.
• Build monitoring, observability, and feedback loops.
• Design and optimize prompt templates and system instructions.
• Optimize systems for accuracy, latency, and cost.