ML Ops Engineer at JK Technosoft Ltd · Bengaluru (Bangalore) · 3 - 5 years · ₹5L - ₹15L / yr · Profitable · Posted 23 Jun 2023
Data Science
Machine Learning (ML)
Python
Git
Docker
Kubernetes
Jenkins
Amazon Web Services (AWS)
PostgreSQLRoles and Responsibilities:
- Design, develop, and maintain the end-to-end MLOps infrastructure from the ground up, leveraging open-source systems across the entire MLOps landscape.
- Creating pipelines for data ingestion, data transformation, building, testing, and deploying machine learning models, as well as monitoring and maintaining the performance of these models in production.
- Managing the MLOps stack, including version control systems, continuous integration and deployment tools, containerization, orchestration, and monitoring systems.
- Ensure that the MLOps stack is scalable, reliable, and secure.
Skills Required:
- 3-6 years of MLOps experience
- Preferably worked in the startup ecosystem
Primary Skills:
- Experience with E2E MLOps systems like ClearML, Kubeflow, MLFlow etc.
- Technical expertise in MLOps: Should have a deep understanding of the MLOps landscape and be able to leverage open-source systems to build scalable, reliable, and secure MLOps infrastructure.
- Programming skills: Proficient in at least one programming language, such as Python, and have experience with data science libraries, such as TensorFlow, PyTorch, or Scikit-learn.
- DevOps experience: Should have experience with DevOps tools and practices, such as Git, Docker, Kubernetes, and Jenkins.
Secondary Skills:
- Version Control Systems (VCS) tools like Git and Subversion
- Containerization technologies like Docker and Kubernetes
- Cloud Platforms like AWS, Azure, and Google Cloud Platform
- Data Preparation and Management tools like Apache Spark, Apache Hadoop, and SQL databases like PostgreSQL and MySQL
- Machine Learning Frameworks like TensorFlow, PyTorch, and Scikit-learn
- Monitoring and Logging tools like Prometheus, Grafana, and Elasticsearch
- Continuous Integration and Continuous Deployment (CI/CD) tools like Jenkins, GitLab CI, and CircleCI
- Explain ability and Interpretability tools like LIME and SHAP
Shubham Vishwakarma
Full Stack Developer - Averlon
About JK Technosoft Ltd
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Machine Learning (ML)PythonAmazon Web Services (AWS)- Bachelor’s degree in computer science, Data Science, Information Systems, or a related field
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Machine Learning (ML)PythonGreetings!
Hiring For Large Product Based Company!
Role- Mlops Engineer
Experience- 8-12 years
Location- Pune, Nagpur
JD-
- 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering
- Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure
Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn
Designation: Lead MLOps Engineer (GPU Optimization)
About CloudKeeper:
CloudKeeper is a cloud cost optimization partner that combines the power of group buying & commitments management, expert cloud consulting & support, and an enhanced visibility & analytics platform to reduce cloud cost & help businesses maximize the value from AWS, Microsoft Azure, & Google Cloud.
A certified AWS Premier Partner, Azure Technology Consulting Partner, Google Cloud Partner, and FinOps Foundation Premier Member, CloudKeeper has helped 350+ global companies save an average of 20% on their cloud bills, modernize their cloud set-up and maximize value — all while maintaining flexibility and avoiding any long-term commitments or cost.
CloudKeeper hived off from TO THE NEW, a digital technology service company with 2500+ employees and an 8-time GPTW winner.
To know more, please visit - https://www.cloudkeeper.com/
Responsibilities:
- Drive R&D and engineering for AI Infrastructure optimization within CloudKeeper's FinOps for AI platform — building the Tuner AI / Commit AI capability on GPU and ML workloads
- Design and build optimization engines for GPU right-sizing, idle shutdown, spot migration with checkpoint/resume automation, inference batching, quantization, and model placement
- Extend the optimization stack to LLM-era workloads — caching, model routing, dynamic batching, prompt optimization, RAG-aware architectures
- Partner with the Lens AI team to translate GPU and ML workload signals into actionable, dollar-quantified optimization recommendations for customers
- Work cross-functionally with product, platform, and customer success teams to ship optimization features end-to-end (data ingestion → optimization engine → customer-facing recommendation)
- Lead technical direction for AI workload optimization, set engineering standards, and mentor the ML / MLOps engineering bench as the AI Infrastructure pillar scales
- (Lead level) Hire, ramp, and grow a team of ML infrastructure engineers as headcount expands
Must Have:
- B.E / B.Tech / M.Tech / MCA with 7+ years of hands-on engineering experience
- Production experience with GPU workloads — has measurably optimized GPU utilization, throughput, or cost in a real production environment, not just academic / lab work
- Strong performance engineering background — must come ready with a concrete optimization story including before/after metrics (latency, throughput, or cost reduction)
- Strong Python + Linux + systems fundamentals
- Solid understanding of the ML model lifecycle — training, serving, inference — able to reason about what is running on the GPU and why
- MLOps fluency — model deployment, monitoring, observability, GPU cluster operations
- Hands-on with cloud GPU instances (AWS P5 / G6, Azure ND series, GCP A3, or equivalent) and Kubernetes-based GPU orchestration (EKS / AKS / GKE GPU node pools, Karpenter, Run:ai, NVIDIA GPU Operator, or similar)
- Familiarity with at least one modern LLM inference framework — vLLM, TGI, Triton, SGLang, Ray Serve, or BentoML
- Strong communication skills — able to translate deep technical optimization into customer / business outcomes
- (Lead level) Experience managing or technically leading a team of 3+ engineers
Good to Have:
- Deep LLM-era optimization expertise — KV caching, semantic caching, model routing, dynamic batching, quantization (FP16 → INT8 → INT4), model distillation, structured outputs
- Familiarity with LLM workload patterns — RAG, agents, embeddings, vector databases (Pinecone, Weaviate, Qdrant)
- CUDA, NCCL, mixed-precision training and inference
- Experience with managed ML training platforms — SageMaker, Azure ML, Vertex AI, Databricks Mosaic
- Exposure to GPU-native clouds — CoreWeave, Lambda Labs, RunPod, Crusoe
- Open source contributions to ML infrastructure projects — vLLM, llama.cpp, TGI, Ray, Triton, KubeRay
- Adjacent experience in cloud cost optimization / FinOps — Spot.io, ScaleOps, Granulate, CAST AI
- Comfort with Agile methodology and modern engineering practices (CI/CD, code review, observability)
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon