Staff Engineer

Grid Dynamics wants to build a centralized, observable and secure platform for their ML, Computer Vision, LLM and SLM models. Grid Dynamics wants to onboard a vast number of AI agents, able to cover multiple required skills, ensuring a certain level of control and security in regards to their usage and availability. The observable platform must be vendor-agnostic, easy to extend to multiple type of AI applications and flexible in terms of technologies, frameworks and data types.

This project is focused on establishing a centralized LLMOps capability where every ML, CV, AI-enabled application is monitored, observed, secured and provides logs of every activity.

The solution consists of key building blocks such monitor every step in a RAG, Multimodal RAG or Agentic Platform, track performances and provide curated datasets for potential fine-tuning.

Alignment with business scenarios, Grid Dynamics provides also certain guardrails that allow or block interactions user-to-agent, agent-to-agent or agent-to-user. Also, Guardrails will enable predefined workflows, aimed to give more control over the series of LLM chains.

Responsibilities

Job Role: Lead MLOps Engineer

Location: Hyderabad/Bangalore/Chennai

Experience: 7+ Years

Roles & Responsibilities

• Chunking (primarily focused on VectorDB storage).
• Document Parsing and OCR
• Document Parsing with VLMs (Vision Language Models)
• Function Calling with LLMs
• Retrieval Augmented Generation
• Traditional Search (BM25, NER based parsers, Keyword based search index)
• Semantic Search (Embeddings, Embedding models)
• Fine Tuning using LoRA
• Merging multiple LoRA adapters using MergeKit
• Quantising LLMs
• Prompt Engineering techniques
• 4+ years with Azure (ML pipeline components), Azure Databricks, Azure DevOps.
• Proven experience of design and deployment of end-to-end ML pipelines.
• Experience with building infrastructure for classic DS models and/or LLM/SLMs.
• 4+ years with orchestration (e.g., Kubeflow, Airflow, Azure Data Factory) and CI/CD for ML.
• Experience deploying containerized ML solutions (e.g. Docker/Kubernetes).
• Knowledge of model and data versioning (e.g. MLflow, DVC).
• Knowledge of MLSecOps (security in the context of MLOps)
• Experience with Infrastructure as a Code (e.g., Terraform, CloudFormation).
• Knowledge of MLOps for LLM/SLM
• Experience in ML system/architecture design (load balancing, caching, failover).
• Knowledge in building scalable, resilient ML architectures.
• Cross-team collaboration experience (data science, engineering, DevOps).
• Experience with monitoring/logging for production models (e.g. Prometheus, Grafana, ELK stack).
  
  

Requirements

• 7+ years in ML Ops / DevOps / data engineering
• Strong Python skills, plus experience with MLflow, Kubeflow, or Airflow
• Hands-on with Docker, Kubernetes, and cloud platforms
• Knowledge of data and model versioning tools (e.g., DVC, MLflow)
  
  

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office
  
  

About Us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

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