HPC Expert ClearML & Confidential Computing

Title                                 : Senior AI Platform / MLOps Engineer

Experience                    : 6+ years

Work type                      : Chennai - Work from Office/other locations - Remote

Employment Type      : Full Time

Notice Period              : Immediate

Work Day                     :Mon to Fri

Key Responsibilities:

• Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control-plane topologies
• Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management
• Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression
• GitOps CI/CD, container registry, artifact/model versioning, environment promotion; observability and audit wiring (Splunk, Prometheus/Grafana)
• Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline
• Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

Technical Skills:

• 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred
• Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM — you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory-bandwidth trade-offs
• GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks
• Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

• Azure or AWS GPU compute operations experience

Strongly preferred

• NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards 

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

Role Overview

We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.

Key Responsibilities

·       Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

·       GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

·       Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

·       MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

·       Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

·       Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

·       Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

·       Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

·       GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

·       Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

·       Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

·       Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

·       Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

·       Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

·       Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

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)

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 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  

Greetings!

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  

Job Description: Lead - Cloud Engineering (AWS / Azure)

Role Title: Lead - Cloud Engineering

Experience Level: 10+ Years

Domain Focus: Healthcare AI & Cloud Infrastructure

Location: Remote

Job Overview

We are seeking an experienced Lead - Cloud Engineering with over 10 years of IT experience to lead our cloud strategy, architecture, and infrastructure teams. In this role, you will oversee end-to-end cloud deployment, multi-cloud migration, and scalable architecture designed to support cutting-edge Generative AI applications in the healthcare technology domain.

The ideal candidate brings deep technical expertise in both AWS and Azure, strong hands-on capability in cloud infrastructure, and proven leadership experience driving security, compliance, and team growth.

Key Responsibilities

Cloud Architecture & Migration

• Lead the architecture, design, and execution of cloud migrations, deployments, and modernizations across AWS and Azure environments.
• Drive Infrastructure as Code (IaC) standards using Terraform, CloudFormation, or Bicep to ensure scalable, automated infrastructure provisioning.
• Build high-availability, low-latency architectures optimized for data-intensive Generative AI and Machine Learning workloads.

Security & Healthcare Compliance

• Enforce healthcare security standards including HIPAA, HITRUST, SOC 2, and data governance best practices across all cloud assets.
• Implement Zero-Trust security, Identity Access Management (IAM), data encryption key management, and continuous vulnerability monitoring.

Leadership & Team Management

• Manage, mentor, and scale a high-performing team of DevOps, Cloud, and SRE Engineers.
• Drive Agile workflows, sprint planning, incident response frameworks, and SLA compliance.
• Collaborate closely with Data Engineering, AI/ML, and Software Product teams to align infrastructure with business roadmaps.

Operations & FinOps

• Establish cloud cost optimization strategies (FinOps) to manage computing costs associated with AI models and large-scale data processing.
• Manage monitoring, alerting, and telemetry frameworks (e.g., Prometheus, Datadog, CloudWatch) to ensure 99.99% uptime.

Key Requirements

• Experience: 10+ years of overall IT experience with at least 5+ years in a cloud leadership or lead architect role.
• Cloud Platforms: Advanced hands-on expertise with both AWS (e.g., EC2, S3, EKS, Bedrock, SageMaker) and Azure (e.g., AKS, Azure OpenAI, Blob, Virtual Machines).
• DevOps & IaC: Strong background in Terraform, Docker, Kubernetes, CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins).
• Domain Knowledge: Prior experience building or managing cloud environments within Healthcare, Life Sciences, or HealthTech is strongly preferred.
• AI/ML Familiarity: Experience supporting cloud infrastructure for machine learning pipelines, LLM deployments, or GPU compute management.
• Certifications (Preferred): AWS Certified Solutions Architect – Professional, Azure Solutions Architect Expert, or Certified Kubernetes Administrator (CKA).

About us

MyRico builds personal AI agents for enterprise, the copilots and digital employees that make humans more productive. The MyRico agents sit at the intersection of enterprise memory, high-end security, and an ever-expanding set of capabilities. We're a small team shipping fast, and the product is live with real customers today.

The role

Full-time · Bangalore

You'll own the systems that make an autonomous agent trustworthy in production. This is not just prompt engineering, and it's not model training - it's that and all the engineering layers in between: model steering, memory architecture, orchestration design, latency optimization, deterministic vs non-deterministic systems tradeoff, deployment, and operator tooling.

Concretely, the kind of work you'd have done here last week would include enhancing agent memory systems, runtime and scheduling reliability and predictability, adding new capability and tools to deployed agents, operator tools, live system debugging and benchmarking various models for price, latency and quality. And that is just last week. We are a small nimble startup rapidly working to address customer needs in this growing space, so things change rapidly.

What we're looking for

• More than 7 years of software engineering, with real production ownership of distributed or stateful systems - you've been paged for something you built and made it not happen again.
• Strong understanding of LLM based native app building, combining classic and model driven applications to get the best of both. You've built on LLMs beyond demos: agent frameworks, tool use, context management, eval fixtures, and you know why "it worked in the transcript" isn't evidence.
• Python and shell in production settings; comfortable in TypeScript/Node. You write boring, testable code and prefer the standard library to a new dependency.
• Systems taste: append-only logs, idempotent reconciliation, fold-the-events state machines, and read-only debugging surfaces feel like home.
• Evidence discipline: tests before features, claims backed by quoted observations, decisions written down.

Nice to have

• Experience running the combination of multi-tenant and single-tenant / on-prem-style fleets with ability to handle per-customer isolation, upgrade paths, migration compatibility in both setups.
• Security instincts for products that touch highly sensitive data and systems, including things like executives' email, calendars, and messages: least privilege, loopback-only services, secrets that never hit a log.
• You already orchestrate AI coding agents in your own workflow and have opinions about where they break.

How we work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation, your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.

Job Title: Java AWS Kubernetes DevOps AI/ML

Experience: 8–10 Years

The candidate should have at least 1 year of experience in AI/ML and hands-on experience with the below technologies:

Java

AWS

Kubernetes

DevOps

AI/ML

MongoDB / PostgreSQL

Key-Value Caching

Vector Databases – ChromaDB / Pgvector

Key Responsibilities:

• Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
• Optimize model performance and ensure scalability in production environments.
• Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
• Conduct data analysis to identify trends and insights.
• Implement MLOps practices for versioning, monitoring, and automating ML workflows.
• Research and evaluate new AI/ML techniques, tools, and frameworks.
• Document system architecture, model design, and development processes.

Required Skills:

• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
• Hands-on experience in building and deploying, finetuning ML/DL models in production.
• Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
• Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
• Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
• Strong analytical skills and ability to work with large datasets.
• Excellent communication and problem-solving abilities.
• Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.).
• Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.

Educational Qualification:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred

Experience - 4 to 6 year

Location – Ahmedabad/Pune/Indore

• Additional Job Description

Additional Job Description

Required Skills and Experience: 

• Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
• Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
• Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
• Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
• Demonstrated experience implementing content filtering / moderation systems.
• Solid skills working with structured and unstructured data and advanced feature engineering.
• Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
• Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
• Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
• Good knowledge of security, data governance, and privacy best practices for AI systems.