Role: MLOps Engineer (JLT)
Location: Bangalore (Working from Office / Hybrid)
About Us: GyanSys is a global mid-tier systems integrator company for over 20 years headquartered in the US with 3000+ employees across 12 countries serving 160+ active enterprise customers across Manufacturing, Industrial, Consumer, Life Sciences and High-Tech industries.
GyanSys provides Digital Transformation services leveraging SAP, Salesforce, Databricks, Snowflake, Application Modernization and various AI projects.
Job Description:
We are seeking a hands-on AI Deployment Engineer specializing in ML Engineering, Model Deployment, Model Governance, and Model Observability. The engineer will own the complete lifecycle of Deep Learning models, LLMs, and SLMs across cloud, on-premises, hybrid, and air-gapped environments.
Scope of Work:
Build and manage MLOps and LLMOps pipelines.
Deploy, host, and scale Deep Learning models, LLMs, and SLMs and Inference optimisation
Manage end-to-end model lifecycle including versioning, deployment, rollout, rollback, and retirement.
Host models on Databricks, Kubernetes, OpenShift, and GPU-based infrastructure.
Implement model governance, lineage, approval workflows, and compliance controls.
Build model monitoring, observability, tracing, logging, and drift detection capabilities.
Optimize model performance, latency, throughput, GPU utilization, and cost.
Support cloud, on-premises, hybrid, and air-gapped environments.
Must-Have Skills
3–5 years in MLOps, LLMOps, ML Engineering, or AI Engineering.
Strong Python development skills.
Hands-on experience with Databricks and/or Azure ML.
Experience with Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
Experience deploying models built using PyTorch and TensorFlow.
Strong expertise in model deployment on:
o Kubernetes
o Databricks
o GPU Infrastructure
Experience with:
o vLLM
o Triton Inference Server
o Ray Serve
o SGLang
o Databricks Model Serving
Strong GPU knowledge including NVIDIA GPUs, CUDA, multi-GPU deployments, and inference optimization.
Experience in Model Registry, Model Governance, Model Monitoring, Drift Detection, and AI Observability.
Strong database knowledge (SQL Server, PostgreSQL, Oracle, MySQL, MongoDB).
Experience with Vector Databases (Pinecone, Chroma, FAISS, Milvus, Azure AI Search).
REST APIs, WebSockets, Streaming HTTP.
Experience with MLflow, OpenTelemetry, LangFuse, Splunk, and Grafana/ELK.
CI/CD using Jenkins, Azure DevOps.
Experience across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
Experience with Auth setup like Keycloak
Good-to-Have Skills
Kafka, RabbitMQ, Event Hub
Fine-tuning and model optimization
Model Governance & Security
Experience with Llama, Mistral, DeepSeek, Qwen, Phi, and Gemma models