LLMOps Engineer

Tekskills; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Tekskills seeks an LLMOps Engineer to architect GenAI platforms that are secure, scalable, and compliant. You will lead governance, risk assessments, and CI/CD-based lifecycle management for models across multiple providers and environments.

The role emphasizes integration of OpenAI, Google, Azure OpenAI, and other LLMs, plus memory optimization, prompt engineering, and cost-efficient operations at scale. Bengaluru-based, on-site role.

Qualifications

• 4+ years in MLOps, DevOps, or platform engineering with 1-2 years in LLM/GenAI environments.
• Deep understanding of LLMs, GenAI agents, prompt engineering, and inference optimization.
• Experience with vector databases and RAG pipelines is beneficial.
• Proficiency in Python and containerization (Docker).

Responsibilities

• Design governance frameworks for GenAI platforms and ensure regulatory compliance.
• Enforce responsible AI practices including fairness, transparency, and auditability.
• Implement robust security: IAM, data encryption, secure API access, and model sandboxing.
• Build scalable LLMOps pipelines for training, fine-tuning, evaluation, deployment, and monitoring.
• Automate lifecycle management with CI/CD, versioning, rollback, and observability.
• Integrate GenAI agents capable of reasoning, planning, and tool use.

Skills

MLOps experience

GenAI platforms

Python

Cloud platforms

FC development

Education

Bachelor's or Master's in CS/Engineering

Tools

Docker

LangChain

LlamaIndex

Langraph

MLflow

Job description

Job Title: LLMOps Engineer GenAI Platforms

Location: Bangalore

Minimum: 3 years of relevant experience

Maximum: 5 years total experience

Must include:

• Strong MLOps experience
• Hands-on with LLMs for at least 2 years OR
• Transitioned from MLOps to LLMOps with understanding of LLM lifecycle

About the Role:

We are looking for a forward-thinking LLMOps Engineer to join our team and help build the next generation of secure, scalable, and responsible Generative AI (GenAI) platforms. This role will focus on establishing governance, security, and operational best practices while enabling development teams to build high-performing GenAI applications. You will also work closely with GenAI agents and integrate LLMs from multiple providers to support diverse use cases.

Key Responsibilities:

• Design and implement governance frameworks for GenAI platforms, ensuring compliance with internal policies and external regulations (e.g., GDPR, AI Act).
• Define and enforce responsible AI practices including fairness, transparency, explainability, and auditability.
• Implement robust security protocols including IAM, data encryption, secure API access, and model sandboxing.
• Collaborate with security teams to conduct risk assessments and ensure secure deployment of LLMs.
• Build and maintain scalable LLMOps pipelines for model training, fine-tuning, evaluation, deployment, and monitoring.
• Automate model lifecycle management with CI/CD, versioning, rollback, and observability.
• Develop and manage GenAI agents capable of reasoning, planning, and tool use.
• Integrate and orchestrate LLMs from multiple providers (e.g., OpenAI, Anthropic, Cohere, Google, Azure OpenAI) to support hybrid and fallback strategies.
• Optimize prompt engineering, context management, and agent memory for production use.
• Ensure high availability, low latency, and cost-efficiency of GenAI workloads across cloud and hybrid environments.
• Implement monitoring and alerting for model drift, hallucinations, and performance degradation.
• Partner with GenAI developers to embed best practices and reusable components (SDKs, templates, APIs).
• Provide technical guidance and documentation to accelerate development and ensure platform consistency.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 4+ years of experience in MLOps, DevOps, or platform engineering, with 1-2 years in LLM/GenAI environments.
• Deep understanding of LLMs, GenAI agents, prompt engineering, and inference optimization.
• Experience with LangChain, LlamaIndex, Langraph or similar agent frameworks.
• Hands-on with MLflow, or equivalent tools.
• Proficient in Python, containerization (Docker) and cloud platforms (AWS/GCP/Azure).
• Familiarity with AI governance frameworks and responsible AI principles.
• Experience with vector databases (e.g., FAISS, Pinecone), RAG pipelines, and model evaluation frameworks.
• Knowledge of Responsible AI, red‑team­ing, and OWASP security priciples.