ml ops engineer

Luxoft

<div><div><h5 class="jobSectionHeader"><b>Project description
</b></h5><p>We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.<br>
The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil &amp; gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.
</p></div><div><h5 class="jobSectionHeader"><b>Responsibilities</b></h5>
<div><div><p>Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
</p></div><div><p>Implement model governance strategies&mdash;versioning, rollback, audit trails, and explainability.
</p></div><div><p>Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
</p></div><div><p>Collaborate with software, IT, and data teams to ensure model integration and compliance.
</p></div><div><p>Support continuous improvement of ML lifecycle management practices and tooling.
</p></div><div><p>business trip to Kuwait
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Skills</b></h5>
<p>Must have
</p><div><div><p>Strong software engineering background with ML Ops or DevOps exposure
</p></div><div><p>Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
</p></div><div><p>Proficiency in CI/CD workflows and scripting for automation.
</p></div><div><p>Familiarity with model monitoring, drift detection, and alerting frameworks.
</p></div><div><p>Experience deploying Python-based ML models in production.
</p></div></div><p>Nice to have
</p><div><div><p>Understanding of Digital Oilfield environments or OT/IT integration.
</p></div><div><p>Familiarity with cloud-native services&mdash;Azure is preferred.
</p></div><div><p>Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
</p></div><div><p>Understanding of security, compliance, and data access protocols in enterprise environments.
</p></div><div><p>Certifications:
</p></div><div><p>Azure DevOps or Azure AI Engineer certification is a plus.
</p></div><div><p>AWS DevOps Engineer certification is nice to have but not required
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Other</b></h5>
<div><div><div><p>Languages</p>
<p>English: C1 Advanced
</p></div></div><div><div><p>Seniority</p>
<p>Senior</p></div></div></div></div></div>
<br><div><div><div><p>Remote Egypt, Egypt
</p></div><p>Req. VR-118737
</p></div><div></div><div><div><p>DevOps
</p></div><div><p>Cross Industry Solutions</p>
</div><div><p>06/11/2025
</p></div><div><p>Req. VR-118737</p></div></div></div><p></p>

ML Ops Engineer

Tata Consultancy Services (TCS)

<div></div><div><div><div><div><p><b>Greetings from TCS!!!</b></p>
<p>
TCS presents an excellent opportunity for Techno Functional Role!</p>
<p><b>
Role: ML Ops Engineer</b></p>
<p><b>
Desired Experience Range: 5-10 Years</b></p>
<p><b>
Location: PAN</b></p>
<p>
Desired Competencies (Technical/Behavioral Competency)</p>
<div><b>
Must-Have</b><br>
<br>
<ul><li>Several years of practical experience in applying machine learning and data science methods in a business context</li></ul>
<br>
<ul><li>Profound knowledge of relevant programming languages and frameworks (e.g., Python, R, scikit-learn, PyTorch)</li></ul>
<br>
<ul><li>Experience working with databases (e.g., SQL) and ideally big data technologies (e.g., GCP Big Query).</li></ul>
<br>
<ul><li>Ideally, expertise in neural networks, especially autoencoders and transformers, to elevate our solutions to the next level.</li></ul>
<br>
<ul><li>Experience with cloud platforms and CI/CD processes (e.g. Github Actions) for data products is advantageous.</li></ul>
<br>
<ul><li>Extensive knowledge of the banking industry, mainly in client transactions, transaction monitoring using anomaly detection algorithms.</li></ul>
<br>
<ul><li>Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP)</li></ul>
<br>
<ul><li>Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift</li></ul>
<br>
<ul><li>Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.</li></ul>
<br>
<ul><li>Ability to understand tools used by data scientist and experience with software development and test automation</li></ul>
<br>
<ul><li>Fluent in English, good communication skills and ability to work in a team</li></ul></div><br>
<p></p>
<div><b>
Key responsibilities:</b><br>
<br>
<ul><li>Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)</li></ul>
<br>
<ul><li>Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools</li></ul>
<br>
<ul><li>Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality</li></ul>
<br>
<ul><li>Data science models testing, validation and tests automation</li></ul>
<br>
<ul><li>Communicate with a team of data scientists, data engineers and architect, document the processes</li></ul></div>
</div></div></div><div><div><div><b>Location</b></div>
<div>Mumbai</div>
</div><div><div><b>Job Function</b></div>
<div>TECHNOLOGY</div>
</div><div><div><b>Role</b></div>
<div>Developer</div>
</div><div><div><b>Job Id</b></div>
<div>368335</div>
</div><div><div><b>Desired Skills</b></div>
<div>Machine Learning | Python | AutoPIPE</div>
</div></div><p>Desired Candidate Profile
</p><div><div><b>Qualifications</b> : BACHELOR OF ENGINEERING</div></div></div><div></div>

TechFlitter Solutions

<p><b>Job Title:</b> AI/ML Ops Engineer<br/><b>Location:</b> Ahmedabad - Onsite<br/><b>Duration:</b> 2-4 years experinece</p><p><b>About the Role</b></p><p>We are seeking an experienced <b>AI/ML Ops Engineer</b> to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications.</p><p><b>Key Responsibilities</b></p><ul><li>Develop and maintain machine learning models to forecast user behavior using structured time-series data.</li><li>Build and optimize end-to-end regression pipelines using advanced libraries such as <b>CatBoost</b>, <b>XGBoost</b>, and <b>LightGBM</b>.</li><li>Design and implement <b>RAG (Retrieval-Augmented Generation)</b> pipelines for enterprise chatbot systems utilizing tools like <b>LangChain</b>, <b>LLM Router</b>, or custom-built orchestrators.</li><li>Work with <b>vector databases</b> for semantic document retrieval and reranking.</li><li>Integrate external APIs into <b>LLM workflows</b> to enable tool/function calling capabilities.</li><li>Package and deploy ML services using tools such as <b>Docker</b>, <b>FastAPI</b>, or <b>Flask</b>.</li><li>Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices.</li></ul><p><b>Core Technologies &amp; Tools</b></p><ul><li><b>Languages:</b> Python (primary), Bash, SQL</li><li><b>ML Libraries:</b> scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow</li><li><b>LLM &amp; RAG Tools:</b> LangChain, Hugging Face Transformers, LlamaIndex, LLM Router</li><li><b>Vector Stores:</b> FAISS, Weaviate, Chroma, Pinecone</li><li><b>Deployment &amp; APIs:</b> Docker, FastAPI, Flask, Postman</li><li><b>Infrastructure &amp; Version Control:</b> Git, GitHub, CI/CD pipelines</li></ul><p><b>Preferred Qualifications</b></p><ul><li>Proven experience in ML Ops, AI infrastructure, or productionizing ML models.</li><li>Strong understanding of large-scale ML system design and deployment strategies.</li><li>Experience working with vector databases and LLM-based applications in production.</li></ul><p>Job Type: Full-time</p><p>Pay: Up to &#8377;75,229.87 per month</p><p>Benefits:</p><ul><li>Provident Fund</li></ul><p>Experience:</p><ul><li>AI/ML: 3 years (Preferred)</li><li>ML OPs: 3 years (Preferred)</li><li>AWS: 1 year (Preferred)</li><li>Python: 3 years (Preferred)</li></ul><p>Work Location: In person</p>

Sr. AI/ML OPs Engineer

Apple

<p>Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other&rsquo;s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It&rsquo;s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you&rsquo;ll do more than join something - you&rsquo;ll add something.<br>
<br>
Apple Pay brought mobile payment to millions of customers, and it&rsquo;s just the beginning. We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team&rsquo;s vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization.<br>
<br>
<b>Description</b><br>
As data operations engineer, you will be a key member of Wallet Payments &amp; Commerce Data Foundations team, and will collaborate with various infrastructure teams, platforms, product, engineering and scientists, to identify requirements that will derive the creation of sensible operations to build and manage data, AML Foundations. The ideal candidate is a self-motivated teammate with good problem solving and communication skills with the ability to adapt and learn quickly, provide results with limited direction, and choose the best possible operational decisions.&quot;,&quot;responsibilities&quot;:&quot;Lead day-to-day operations to ensure organizational delivery of quality results.<br>
<br>
You will be responsible for provisioning, enabling, scaling and maintaining our team&rsquo;s data and ML infrastructures for batch and real time systems including pipelines, frameworks, tools and services in hybrid cloud.<br>
<br>
Shepherd zero-downtime deployment process through continuous delivery practices, rapidly releasing features that provide critical and faster insights to business users.<br>
<br>
Collaborate with the platform team by building the right tools for observability, monitoring, alerting and self-healing for the day to day management of analytics foundations.<br>
<br>
Enable ML platform for larger adoption across wallet product teams to bring advanced analytics capabilities.<br>
<br>
Manage, deploy and scale various tiered data stores for supporting various data products.<br>
<br>
Debug complex problems in distributed environment and ability to run the prod incidents efficiently to following up with Post incident reviews.<br>
<br>
Developing self-service tools and automation to improve engineering efficiency and the quality of services.<br>
<br>
<b>Preferred Qualifications</b><br>
Excellent verbal and written communication skills<br>
<br>
Self-starter with forward thinking capability with strong executional track record and be accountable for business priorities<br>
<br>
Hands-on experience with CI/CD pipelines and cloud environments like Gitlab, Spinnaker , Docker/Kubernetes observability using Splunk, Grafana/Data dog is a huge plus.<br>
<br>
Experience scaling operations in a fast-paced and dynamic environment. Experience working in agile or evolving product environments<br>
<br>
<b>Minimum Qualifications</b><br>
5+ years of professional experience with large scale platform operations and support<br>
<br>
3+ years of experience in developing and operating infrastructure, data platforms and cloud services in public cloud environments (AWS or GCP)<br>
<br>
Proficient with one or more programming languages such as Python/Go/Rust/Java/Scala for automation and API integrations<br>
<br>
Experience in implementing security controls, governance processes, compliance validation, infrastructure cost analysis and optimization<br>
<br>
Hands on experience in network protocol, load balancing technologies and transport protocols and Linux/Unix system internals and scripting.<br>
<br>
Knowledge of analytics and Applied ML stack like Apache Spark, Trino/Pinot, Iceberg, Atlas, Flink, Airflow/Luigi, MLFlow, Data Catalogs, Jupyter Notebooks, Vector database and Cassandra
<br>
<br>
Solid understanding of IAAC (infrastructure as a code) techniques like terraform, orchestration and tooling.&quot;,&quot;internalDetails&quot;:null,&quot;eeoContent&quot;:null</p>

Data & ML Ops Engineer

Movius Interactive

<div><h1 class="jobSectionHeader"><b>Job Title: ML Ops Engineer &ndash; Speech AI &amp; Infrastructure (5&ndash;8 Years)</b></h1>
<h2 class="jobSectionHeader"><b>Location: Bangalore (India)</b></h2>
<h2 class="jobSectionHeader"><b>Full-Time | Immediate Joiners Preferred</b></h2>
<h3 class="jobSectionHeader"><b>Role Summary:</b></h3>
<p>We are looking for an experienced ML Ops Engineer (5&ndash;8 years) with a strong background in operationalizing AI/ML systems, particularly in speech AI agents integrated into telephony environments. This role bridges AI infrastructure, voice bot deployment, and real-time streaming operations, enabling AI models to function reliably in production use cases such as customer collections, voice-based load bookings, and conversational AI in logistics and telecom.</p>
<h3 class="jobSectionHeader"><b>Key Responsibilities:</b></h3>
<ul><li>Build, deploy, and maintain real-time speech AI agents for telephony (inbound/outbound) using CPaaS platforms and audio pipelines.</li>
<li>Integrate ASR (Automatic Speech Recognition), TTS (Text-to-Speech), and LLMs with voice bot workflows using WebSocket or RTP streams.</li>
<li>Work closely with backend engineers and AI scientists to deploy scalable ML models into production across Kubernetes, Docker, and cloud-native environments (AWS/GCP).</li>
<li>Set up observability, logging, and monitoring of speech pipelines (latency, dropouts, stream integrity) using Prometheus, Grafana, ELK, etc.</li>
<li>Automate the ML lifecycle and CI/CD for AI models using tools like MLflow, Airflow, or Kubeflow.</li>
<li>Manage streaming latency optimization across ASR, LLM, and TTS chains in low-latency applications like telephony bots.</li>
<li>Ensure fault-tolerant, secure, and compliant deployment of voice-based systems using industry-standard DevOps practices.</li>
</ul><h3 class="jobSectionHeader"><b>Required Skills:</b></h3>
<ul><li>5&ndash;8 years of experience in ML Ops, AI platform engineering, or DevOps with hands-on ML deployment.</li>
<li>Experience with ASR/TTS model integration (e.g., Whisper, Amazon Polly, Google Speech API).</li>
<li>Hands-on experience deploying AI in telephony environments, with working knowledge of Asterisk, FreeSWITCH, or similar systems.</li>
<li>Solid command over WebSocket, SIP, RTP, or similar streaming protocols for voice integration.</li>
<li>Proficient with cloud services (AWS/GCP/Azure), containers (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform).</li>
<li>Experience with end-to-end CI/CD pipelines, GitOps, and model monitoring (accuracy, drift, performance).</li>
<li>Strong programming in Python and scripting to support infrastructure and integration tasks.</li>
</ul><h3 class="jobSectionHeader"><b>Nice to Have:</b></h3>
<ul><li>Exposure to CPaaS platforms (e.g., Twilio, Vonage, Kaleyra, Exotel) for voice bot integration.</li>
<li>Familiarity with conversational AI/NLU platforms and agentic AI frameworks.</li>
<li>Prior experience supporting AI agents in collections, logistics/freight, or telecom workflows.</li>
</ul><h3 class="jobSectionHeader"><b>What We Offer:</b></h3>
<ul><li>Work on next-gen AI voice agents deployed in high-impact customer environments.</li>
<li>Influence platform architecture and deliver cutting-edge infrastructure for real-time ML.</li>
<li>High-ownership, engineering-led culture with strong product alignment and technical mentorship.</li></ul></div>

MLOPS ENGINEER

Cognizant Technology Solutions

<div><p><b>Job Summary</b></p><p>
We are seeking a skilled Software Engineer with 4-6 years of experience to join our dynamic team. The ideal candidate will have expertise in Mainframe CoolGen/CA Gen IBM Mainframe Utilities ML Ops JCL DB2 and Cobol. This role involves working in a hybrid model with rotational shifts focusing on developing and maintaining high-quality software solutions that align with our companys goals.</p><p><b>
Responsibilities</b></p><ul><li>
Develop and maintain software applications using Mainframe CoolGen/CA Gen to meet business requirements.</li><li>
Utilize IBM Mainframe Utilities to ensure efficient processing and data management.</li><li>
Implement ML Ops practices to streamline machine learning workflows and improve model deployment.</li><li>
Write and optimize JCL scripts for job scheduling and execution.</li><li>
Design and manage databases using DB2 to ensure data integrity and performance.</li><li>
Code and debug programs in Cobol to enhance system functionality.</li><li>
Collaborate with cross-functional teams to gather and analyze user requirements.</li><li>
Conduct thorough testing and debugging to ensure software quality and reliability.</li><li>
Provide technical support and troubleshooting for software-related issues.</li><li>
Participate in code reviews to maintain coding standards and best practices.</li><li>
Document software processes and procedures for future reference and training.</li><li>
Stay updated with the latest industry trends and technologies to drive innovation.</li><li>
Work in a rotational shift model to provide continuous support and development.</li></ul><p><b>
Qualifications</b></p><ul><li>
</li><li>Possess a strong understanding of Mainframe CoolGen/CA Gen and its application in software development.</li><li>
Demonstrate proficiency in IBM Mainframe Utilities for effective data management.</li><li>
Have experience in implementing ML Ops practices to enhance machine learning operations.</li><li>
Show expertise in writing and optimizing JCL scripts for efficient job execution.</li><li>
Be skilled in designing and managing databases using DB2.</li><li>
Exhibit strong coding and debugging skills in Cobol.</li><li>
Be able to work collaboratively in a team environment and communicate effectively.</li></ul><p><b>
Certifications Required</b></p><p>
IBM Certified Database Administrator - DB2 Certified Mainframe Professional</p></div>

Mainframe Developer

<div>We work on Apple scale opportunities and challenges. We are engineers at heart. We like solving technical problems. We believe a good engineer has the curiosity to dig into inner workings of technology and is always experimenting, reading and in constant learning mode. If you are a software engineer with passion to code and dig deeper into any technology, love knowing the internals, fascinated by distributed systems architecture, we want to hear from you.<br>
<br>
<br>
<br>
<b>Description</b><br>
We are seeking a highly skilled LLM Ops and ML Ops Engineer to lead the deployment, scaling, monitoring, and optimization of large language models (LLMs) across diverse environments. This role is critical to ensuring our machine learning systems are production-ready, high-performing, and resilient. The ideal candidate will have deep expertise in Python programming / Go Programming, a comprehensive understanding of LLM internals, and hands-on experience with various inference engines and deployment strategies. The person should be capable of exhibiting deftness to balance multiple simultaneous competing priorities and deliver solutions in a timely manner. The person should be able to understand complex architectures and be comfortable working with multiple teams<br>
<br>
KEY RESPONSIBILITIES:<br>
<ul><li>Design and build scalable infrastructure for fine-tuning, and deploying large language models.</li></ul><br>
<ul><li>Develop and optimize inference pipelines using popular frameworks and engines (e.g. TensorRT, vLLM, Triton Inference Server).</li></ul><br>
<ul><li>Implement observability solutions for model performance, latency, throughput, GPU/TPU utilization, and memory efficiency.</li></ul><br>
<ul><li>Own the end-to-end lifecycle of LLMs in production-from experimentation to continuous integration and continuous deployment (CI/CD).</li></ul><br>
<ul><li>Collaborate with research scientists, ML engineers, and backend teams to operationalize groundbreaking LLM architectures.</li></ul><br>
<ul><li>Automate and harden model deployment workflows using Python, Kubernetes, Containers and orchestration tools like Argo Workflows and GitOps.</li></ul><br>
<ul><li>Design reproducible model packaging, versioning, and rollback strategies for large-scale serving.</li></ul><br>
<ul><li>Stay current with advances in LLM inference acceleration, quantization, distillation, and model compilation techniques (e.g., GGUF, AWQ, FP8).</li></ul><br>
<b>
Preferred Qualifications</b><br>
Experience integrating LLMs into micro-services or edge inference platforms.<br>
<br>
Experience with Ray distributed inference
<br>
<br>
Hands-on with quantization libraries<br>
<br>
Contributions to open-source ML infrastructure or LLM optimization tools.<br>
<br>
Familiarity with cloud platforms (AWS, GCP) and infrastructure-as-code (Terraform).<br>
<br>
Exposure to secure and compliant model deployment workflows<br>
<br>
<br>
<br>
<b>Minimum Qualifications</b><br>
5+ years of experience in LLM/ML Ops, DevOps, or infrastructure engineering with a focus on machine learning systems.<br>
<br>
Advance level proficiency in Python/Go, with ability to write clean, performant, and maintainable production code.<br>
<br>
Deep understanding of transformer architectures, LLM tokenization, attention mechanisms, memory management, and batching strategies.<br>
<br>
Proven experience deploying and optimizing LLMs using multiple inference engines.<br>
<br>
Strong background in containerization and orchestration (Kubernetes, Helm).<br>
<br>
Familiarity with monitoring tools (e.g., Prometheus, Grafana), logging frameworks, and performance profiling.<br>
<br>
&quot;,&quot;internalDetails&quot;:null,&quot;eeoContent&quot;:null</div>

LLM Ops Engineer

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