524 Mlops Jobs - Page 15

Senior xOps Specialist AIOps, MLOps & DataOps Architect Location: Chennai, Pune Employment Type: Fulltime - Hybrid Experience Required: 12-15 years Job Summary: We are seeking a Senior xOps Specialist to architect, implement, and optimize AI-driven operational frameworks across AIOps, MLOps, and DataOps. The ideal candidate will design and enhance intelligent automation, predictive analytics, and resilient pipelines for large-scale data engineering, AI/ML deployments, and IT operations. This role requires deep expertise in AI/ML automation, data-driven DevOps strategies, observability frameworks, and cloud-native orchestration. Key Responsibilities – Design & Architecture AIOps: AI-Driven IT Operations & Automation Architect AI-powered observability platforms, ensuring predictive incident detection and autonomous IT operations. Implement AI-driven root cause analysis (RCA) for proactive issue resolution and performance optimization. Design self-healing infrastructures leveraging machine learning models for anomaly detection and remediation workflows. Establish event-driven automation strategies, enabling autonomous infrastructure scaling and resilience engineering. MLOps: Machine Learning Lifecycle Optimization Architect end-to-end MLOps pipelines, ensuring automated model training, validation, deployment, and monitoring. Design CI/CD pipelines for ML models, embedding drift detection, continuous optimization, and model explainability. Implement feature engineering pipelines, leveraging data versioning, reproducibility, and intelligent retraining techniques. Ensure secure and scalable AI/ML environments, optimizing GPU-accelerated processing and cloud-native model serving. DataOps: Scalable Data Engineering & Pipelines Architect data processing frameworks, ensuring high-performance, real-time ingestion, transformation, and analytics. Build data observability platforms, enabling automated anomaly detection, data lineage tracking, and schema evolution. Design self-optimizing ETL pipelines, leveraging AI-driven workflows for data enrichment and transformation. Implement governance frameworks, ensuring data quality, security, and compliance with enterprise standards. Automation & API Integration Develop Python or Go-based automation scripts for AI model orchestration, data pipeline optimization, and IT workflows. Architect event-driven xOps frameworks, enabling intelligent orchestration for real-time workload management. Implement AI-powered recommendations, optimizing resource allocation, cost efficiency, and performance benchmarking. Cloud-Native & DevOps Integration Embed AI/ML observability principles within DevOps pipelines, ensuring continuous monitoring and retraining cycles. Architect cloud-native solutions optimized for Kubernetes, containerized environments, and scalable AI workloads. Establish AIOps-driven cloud infrastructure strategies, automating incident response and operational intelligence. Qualifications & Skills – xOps Expertise Deep expertise in AIOps, MLOps, and DataOps, designing AI-driven operational frameworks. Proficiency in automation scripting, leveraging Python, Go, and AI/ML orchestration tools. Strong knowledge of AI observability, ensuring resilient IT operations and predictive analytics. Extensive experience in cloud-native architectures, Kubernetes orchestration, and serverless AI workloads. Ability to troubleshoot complex AI/ML pipelines, ensuring optimal model performance and data integrity. Preferred Certifications (Optional): AWS Certified Machine Learning Specialist Google Cloud Professional Data Engineer Kubernetes Certified Administrator (CKA) DevOps Automation & AIOps Certification

Expected Notice Period : 15 Days Shift : (GMT+05:30) Asia/Kolkata (IST) Opportunity Type : Office (Hyderabad) Placement Type : Full Time Indefinite Contract(40 hrs a week/160 hrs a month) (*Note: This is a requirement for one of Uplers' client - Vujis) What do you need for this opportunity - Must have skills required: Communication, and Falcon, ChatGPT, LLAMA, LLM, MLOps, neural networks., PyTorch, TensorFlow, AWS, Azure, Docker, GCP, Kubernetes, NLP, Python Vujis is Looking for: Were offering an exciting opportunity for an Machine Learning Engineer/Data Engineer to join our dynamic team at Vujis, a company focused on simplifying international trade. We are a data company working with some of the largest import/export and manufacturing companies in the Asia & Europe. Our data company helps manufacturing companies find new customers, analyze competitors, and source new suppliers internationaly. Key Responsibilities: Design, build, and maintain scalable data pipelines for diverse datasets. Develop and implement advanced Natural Language Processing (NLP) models and neural network architectures. Perform data cleaning, preprocessing, and augmentation to ensure high-quality inputs for machine learning models. Collaborate with cross-functional teams to deploy machine learning models in production environments, adhering to MLOps best practices. Stay updated with the latest advancements in large language models, including ChatGPT, LLAMA, and Falcon, and explore their applications. Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Minimum of 1.5-2 years of professional experience in machine learning, with a focus on NLP and neural networks. Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or similar. Demonstrated experience in designing and managing scalable data pipelines. Strong understanding of MLOps principles and experience deploying models in production environments. Familiarity with large language models like ChatGPT, LLAMA, Falcon, etc. Preferred Skills: Experience with cloud platforms (AWS, Azure, GCP) for machine learning deployments. Knowledge of containerization and orchestration tools such as Docker and Kubernetes. Strong problem-solving abilities and excellent communication skills. Why work with us - - Opportunity to work on groundbreaking projects in AI and machine learning. - Mentorship & Learning Work closely 1-1 with the founders of the company. - Career Growth There's room to be promoted. - Friendly Culture Join a supportive team that values your input and contributions. - Exciting Industry If you're interested in international trade & export which is very outdated and needs to be improved with tech. - Attractive Salary We pay well for high performance individuals. If you're passionate about advancing NLP and machine learning technologies and meet the above qualifications, we'd love to hear from you.

Responsibilities : - Develop and maintain data pipelines using GCP. - Write and optimize queries in BigQuery. - Utilize Python for data processing tasks. - Manage and maintain SQL Server databases. Must-Have Skills : - Experience with Google Cloud Platform (GCP). - Proficiency in BigQuery query writing. - Strong Python programming skills. - Expertise in SQL Server. Good to Have : - Knowledge of MLOps practices. - Experience with Vertex AI. - Background in data science. - Familiarity with any data visualization tool

The AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a deep understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models. You will support a wide range of customers through the "Ideation to MVP" journey, demonstrating proficiency in leading projects and ensuring delivery excellence. Key Responsibilities : Technical & Engineering Leadership : - Develop solutions leveraging GenAI technologies, integrating advanced AI capabilities into cloud-native architectures to enhance system functionality and scalability. - Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments. - Create solutions that fully leverage the capabilities of modern microservice and container-based environments running in public, private, and hybrid clouds. - Contribute to HCL thought leadership across the Cloud Native domain with an expert understanding of open-source technologies (i.e., Kubernetes/CNCF) and partner technologies. - Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Mandatory Skills & Experience. : - A passionate developer with 7+ years of experience in Java, Python, and Kubernetes, comfortable working as part of a paired/balanced team. - Extensive experience in software development, with significant exposure to AI/ML technologies. - Expertise in GenAI frameworks : Proficient in using GenAI frameworks and libraries such as LangChain, OpenAI API, and Hugging Face Transformers. - Prompt engineering : Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance. - Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models. - Proven experience developing complex solutions that leverage cloud-native technologies-featuring container-based, microservices-based approaches; based on applying 12-factor principles to application engineering. - Exemplary verbal and written communication skills (English). - Positive and solution-oriented mindset. - Solid experience delivering Agile and Scrum projects in a Jira-based project management environment. - Proven leadership skills and the ability to lead projects to ensure delivery excellence. Desired Skills & Experience : - Machine Learning Operations (MLOps) : Experience in deploying, monitoring, and maintaining AI models in production environments using MLOps practices. - Data engineering for AI : Skilled in data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data. - AI model fine-tuning : Proficiency in fine-tuning pre-trained models on specific datasets to improve performance for specialized tasks. AI ethics and bias mitigation : - Knowledgeable about ethical considerations in AI and experienced in implementing strategies to mitigate bias in AI models. Knowledgeable about vector databases, LLMs, and SMLs, and integrating with such models. - Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE). - Deep understanding of core practices including DevOps, SRE, Agile, Scrum, Domain-Driven Design, and familiarity with the CNCF open-source community. - Recognized with multiple cloud and technical certifications at a professional level, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat. Verifiable Certification : At least one recognized cloud professional / developer certification (AWS/Google/Microsoft).

We are looking for a talented and driven Generative AI Engineer with 3 years of experience to join our AI team. You will work on designing, developing, and optimizing cutting-edge generative models for a variety of applications, including text, images, and audio Proficiency in Python and relevant ML/AI libraries (TensorFlow, PyTorch, Hugging Face, etc.),GANs, VAEs, Transformers, and Diffusion Models,NLP,computer vision, or audio synthesis,AWS, GCP, Azure,MLOps Develop and optimize generative models (e.g., GANs, VAEs, Diffusion Models, Transformer Models) for various use cases. Location: Chennai, Hyderabad, Kolkata, Pune, Ahmedabad, Remote

Job Overview: Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. We are looking for a highly skilled MLOps Engineer to join our team. As an MLOps Engineer, you will be responsible for designing, implementing, and maintaining the infrastructure that supports the deployment, monitoring, and scaling of machine learning models in production. You will work closely with data scientists, software engineers, and DevOps teams to ensure seamless integration of machine learning models into our production systems. The job is NOT for you if: You dont want to build a career in AI/ML. Becoming an expert in this technology and staying current will require significant self-motivation. You like the comfort and predictability of working on the same problem or code base for years. The tools, best practices, architectures, and problems are all going through rapid change you will be expected to learn new skills quickly and adapt. Key Responsibilities: Model Deployment: Design and implement scalable, reliable, and secure pipelines for deploying machine learning models to production. Infrastructure Management: Develop and maintain infrastructure as code (IaC) for managing cloud resources, compute environments, and data storage. Monitoring and Optimization: Implement monitoring tools to track the performance of models in production, identify issues, and optimize performance. Collaboration: Work closely with data scientists to understand model requirements and ensure models are production ready. Automation: Automate the end-to-end process of training, testing, deploying, and monitoring models. Continuous Integration/Continuous Deployment (CI/CD): Develop and maintain CI/CD pipelines for machine learning projects. Version Control: Implement model versioning to manage different iterations of machine learning models. Security and Governance: Ensure that the deployed models and data pipelines are secure and comply with industry regulations. Documentation: Create and maintain detailed documentation of all processes, tools, and infrastructure. Qualifications: 5+ years of experience in a similar role (DevOps, DataOps, MLOps, etc.) Bachelors or masters degree in computer science, Engineering, or a related field. Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) Strong understanding of machine learning lifecycle, data pipelines, and model serving. Proficiency in programming languages such as Python, Shell scripting, and familiarity with ML frameworks (TensorFlow, PyTorch, etc.). Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.) Experience with CI/CD tools like Jenkins, GitLab CI, or similar Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent) Strong software engineering skills in complex, multi-language systems Comfort with Linux administration Experience working with cloud computing and database systems Experience building custom integrations between cloud-based systems using APIs Experience developing and maintaining ML systems built with open-source tools Experience developing with containers and Kubernetes in cloud computing environments Familiarity with one or more data-oriented workflow orchestration frameworks (MLFlow, KubeFlow, Airflow, Argo, etc.) Ability to translate business needs to technical requirements Strong understanding of software testing, benchmarking, and continuous integration Exposure to machine learning methodology and best practices Understanding of regulatory requirements for data privacy and model governance. Preferred Skills: Excellent problem-solving skills and ability to troubleshoot complex production issues. Strong communication skills and ability to collaborate with cross-functional teams. Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack). Knowledge of database systems (SQL, NoSQL). Experience with Generative AI frameworks Preferred cloud-based or MLOps/DevOps certification (AWS, GCP, or Azure)

We are seeking an experienced GCP MLOps Engineer with a strong background in Data Science and Analytics. The ideal candidate will have at least 5 years of experience working with Google Cloud Platform (GCP), Python, Vertex AI, SQL, and other relevant technologies. This role requires a blend of technical proficiency, analytical skills, and the ability to work collaboratively within a team.

Experience: 4+ years of hands-on experience in deploying and monitoring machine learning models. Basic understanding of ML and data science/modeling aspects. Experience with open-source tools for MLOps, such as Kubernetes, Docker, and Apache Airflow and developing and streamlining workflows, building scalable pipelines, etc. Ensuring successful model development, testing, optimization, scaling, monitoring/observability, and governance Continuous integration and continuous deployment (CI/CD) Proficiency in cloud platforms, especially Azure and/or AWS. Strong scripting skills (e.g., Python) for automation and tool development. Problem-Solving: o Strong problem-solving skills with a focus on finding efficient and scalable solutions. o Ability to learn new tools on the go and developing best practices is mandatory.

We seek a Senior AI Scientist with strong ML fundamentals and data engineering expertise to lead the development of scalable AI/LLM solutions. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities 1. AI/LLM Development: o Fine-tune and optimize LLMs (e.g., GPT, Llama) and traditional ML models for production. o Implement retrieval-augmented generation (RAG), vector databases, and orchestration tools (e.g., LangChain). 2. Data Engineering: o Build scalable data pipelines for unstructured/text data (e.g., Spark, Kafka, Airflow). o Optimize storage/retrieval for embeddings (e.g., pgvector, Pinecone). 3. MLOps & Deployment: o Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes. o Design CI/CD pipelines for LLM workflows (experiment tracking, monitoring). 4. Collaboration: o Work with DevOps to optimize latency/cost trade-offs for LLM APIs. o Mentor junior team members on ML engineering best practices. Required Skills & Qualifications Education: MS/PhD in CS/AI/Data Science (or equivalent experience). Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects.

Design, develop, and deploy AI/ML models; build scalable, low-latency ML infrastructure; run experiments; optimize algorithms; collaborate with data scientists, engineers, and architects; integrate models into production to drive business value. Required Candidate profile 5–10 yrs in AI/ML, strong in model development, optimization, and deployment. Skilled in Azure, ML pipelines, data science tools, and collaboration with cross-functional teams.

Greeting from Linnk Group Title: AI Lead - Generative AI & Deployment Location: Infopark, Kochi, Kerala Employment Type: Permanent About Us: Linnk Group is a global STEM recruitment and technology consultancy with established entities in the UAE, KSA, Qatar, India, and the UK. We are building AI-first platforms that power intelligent automation across recruitment, sales, and workforce management. Our technology stack includes LLM-based applications, RAG pipelines, CV parsing engines, chatbots, and agentic AI workflows integrated with cloud-native infrastructure. We are looking for a hands-on and experienced Lead AI/ML Engineer to architect, build, and scale our GenAI systems across products. This is a foundational role that will help define the technical direction and lead the implementation of production-grade AI components. Strong proficiency in deep learning and machine learning architectures, including ResNets, AlexNet, DenseNets, RCNNs, Vision Transformers (ViT) , Diffusion Models , and GANs In-depth understanding of Transformers and modern LLM architectures (e.g., LLaMA , Mistral , Qwen , GPT , Claude , BERT ), with a focus on NLP and generative AI . Familiarity with legacy models like RNNs and LSTMs is a plus. Expertise in Machine Learning (ML) and Natural Language Processing (NLP) frameworks such as PyTorch, Tensorflow, Hugging Face Transformers, LangChain . Proven experience designing recommendation systems and LLM-based applications , including RAG pipelines , structured generation, and retrieval systems. Skilled in model performance optimization , including quantization, pruning, latency reduction, and memory-efficient inference. Advanced knowledge of embeddings and vector search using tools like FAISS, Milvus, Pinecone, and Quadrant . Hands-on experience with CI/CD pipelines for ML/LLM workflows (e.g., GitHub Actions, Jenkins). In-depth understanding of MLOps and LLMOps practices including model versioning, deployment automation, drift detection, and continuous monitoring. Familiarity with automated evaluation frameworks. Skilled in Docker, Kubernetes , and building scalable microservices. Demonstrated experience deploying and maintaining ML systems on AWS , including SageMaker, EC2, EKS, RDS, and S3 . Ability to optimize and scale LLM inference endpoints , and orchestrate multi-agent workflows in production environments. Deep understanding of vector databases , semantic search , and dense retrieval techniques for real-time matching. Experience integrating with AI agent frameworks like LangGraph, Haystack, and CrewAI , and building robust RAG pipe

8+ years of total experience with 5+ years of professional experience in AI, ML or a related field. Master's or PhD in Computer Science, Mathematics, AI or a related field. Good mathematical understanding of machine learning and deep learning algorithms. Advanced knowledge of statistical and algorithmic models as well as of fundamental mathematical concepts, such as linear algebra and probability. Experience with Natural Language Processing, Deep Learning, and Computer Vision techniques. Proficiency in programming languages such as Python or Scala, along with experience using DL frameworks like TensorFlow, PyTorch etc. Experience working with large data sets and writing efficient code capable of processing large data streams at speed. Experience in program leadership, governance, and change enablement Understand client challenges and propose solutions based on AI/GenAI capabilities. Analyze and explain AI and machine learning (ML) solutions while setting and maintaining high ethical standards. Work on functional design, process design, prototyping, testing, training, and defining support procedures, in collaboration with data engineering team and executive leadership. Train and deploy AI models to solve complex business problems or automate processes. Convert AI/ML models into APIs to be used in other applications. Manage a team in implementing AI solutions and developing products. Collaborate with external partners, such as research institutions or universities, to explore innovative AI approaches and foster collaborative research projects. Ensure compliance with data privacy regulations and ethical guidelines while working with sensitive and confidential data. Stay up to date with the latest developments in machine learning and AI. Experience working with Azure Data & AI services. Experience with MLOps tools like MLFLow, Azure MLOps or similar tools. High level of ownership and creative problem solving under resource constraints. Excellent verbal and written communication skills. Ability to interact with C-Level executives

Role Overview We are seeking a highly skilled AI Engineer with 8-12 years of experience to lead the development of Generative AI (GenAI) and Machine learning solutions for internal projects. This role requires a self-driven leader with exceptional communication, strategic thinking, and expertise in data analytics and visualization to deliver innovative GenAI tools tailored to internal team needs while driving cross-functional collaboration. Key Responsibilities - GenAI Development: Design and develop advanced GenAI models (e.g., LLMs, DALL E models) and AI Agents to automate internal tasks and workflows. Exposure to LLMs: Utilize Azure Open AI APIs, experience on models like GPT4o, O3 , llama3 Enhance the existing RAG based application: In depth understanding of stages of RAG - chunking, retrieval etc. - Cloud Deployment: Deploy and scale GenAI solutions on Azure Cloud services (e.g., Azure Function App) for optimal performance. In depth understanding of ML models like linear regression, random forest, decision trees. In depth understanding on clustering and supervised models. - AI Agent Development: Build AI agents using frameworks like LangChain to streamline internal processes and boost efficiency. - Data Analytics: Perform advanced data analytics to preprocess datasets, evaluate model performance, and derive actionable insights for GenAI solutions. - Data Visualization: Create compelling visualizations (e.g., dashboards, charts) to communicate model outputs, performance metrics, and business insights to stakeholders. Stakeholder Collaboration: Partner with departments to gather requirements, align on goals, and present technical solutions and insights effectively to non-technical stakeholders. - Model Optimization: Fine-tune GenAI models for efficiency and accuracy using techniques like prompt engineering, quantization, and RAG (Retrieval-Augmented Generation). LLMOps Best Practices: Implement GenAI-specific MLOps, including CI/CD pipelines (Git, Azure DevOps) - Leadership: Guide cross-functional teams, mentor junior engineers, and drive project execution with strategic vision and ownership. Helicopters, strategic Thinking**: Develop innovative GenAI strategies to address business challenges, leveraging data insights to align solutions with organizational goals. - Self-Driven Execution: Independently lead projects to completion with minimal supervision, proactively resolving challenges and seeking collaboration when needed. - Continuous Learning: Stay ahead of GenAI, analytics, and visualization advancements, self-learning new techniques to enhance project outcomes. Required Skills & Experience - Experience: 8-12 years in AI/ML development, with at least 4 years focused on Generative AI and AI agent frameworks. - Education: BTech/BE in Computer Science, Engineering, or equivalent (Masters or PhD in AI/ML is a plus). - Programming: Expert-level Python proficiency, with deep expertise in GenAI libraries (e.g., LangChain, Hugging Face Transformers, PyTorch, Open AI SDK) and data analytics libraries (e.g., Pandas, NumPy), sk-learn. Mac - Data Analytics: Strong experience in data preprocessing, statistical analysis, and model evaluation to support GenAI development and business insights. - Data Visualization: Proficiency in visualization tools (e.g., Matplotlib, Seaborn, Plotly, Power BI, or Tableau) to create dashboards and reports for stakeholders. - Azure Cloud Expertise: Strong experience with Azure Cloud services (e.g., Azure Function App, Azure ML, serverless) for model training and deployment. - GenAI Methodologies: Deep expertise in LLMs, AI agent frameworks, prompt engineering, and RAG for internal workflow automation. - Deployment: Proficiency in Docker, Kubernetes, and CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production-grade GenAI systems. - LLMOps: Expertise in GenAI MLOps, including experiment tracking (e.g., Weights & Biases), automated evaluation metrics (e.g., BLEU, ROUGE), and monitoring. Soft Skills: - Communication: Exceptional verbal and written skills to articulate complex GenAI concepts, analytics, and visualizations to technical and non-technical stakeholders. - Strategic Thinking: Ability to align AI solutions with business objectives, using data-driven insights to anticipate challenges and propose long-term strategies. - Problem-Solving: Strong analytical skills with a proactive, self-starter mindset to independently resolve complex issues. - Collaboration: Collaborative mindset to work effectively across departments and engage colleagues for solutions when needed. Preferred Skills - Experience deploying GenAI models in production environments, preferably on Azure Familiarity with multi-agent systems, reinforcement learning, or distributed training (e.g., DeepSpeek). - Knowledge of DevOps practices, including Git, CI/CD, and infrastructure-as-code. Advanced data analytics techniques (e.g., time-series analysis, A/B testing) for GenAI applications. - Experience with interactive visualization frameworks (e.g., Dash, Streamlit) for real-time dashboards. - Contributions to GenAI or data analytics open-source projects or publications in NLP, generative modeling, or data scien

Experience required: 7+ years Core Generative AI & LLM Skills: * 5+ years in Software Engineering, 1+ year in Generative AI. * Strong understanding of LLMs, prompt engineering, and RAG. * Experience with multi-agent system design (planning, delegation, feedback). * Hands-on with LangChain (tools, memory, callbacks) and LangGraph (multi-agent orchestration). * Proficient in using vector DBs (OpenSearch, Pinecone, FAISS, Weaviate). * Skilled in Amazon Bedrock and integrating LLMs like Claude, Titan, Llama. * Strong Python (LangChain, LangGraph, FastAPI, boto3). * Experience building MCP servers/tools. * Designed robust APIs, integrated external tools with agents. * AWS proficiency: Lambda, API Gateway, DynamoDB, S3, Neptune, Bedrock Agents * Knowledge of data privacy, output filtering, audit logging * Familiar with AWS IAM, VPCs, and KMS encryption Desired Skills: * Integration with Confluence, CRMs, knowledge bases, etc. * Observability with Langfuse, OpenTelemetry, Prompt Catalog * Understanding of model alignment & bias mitigation

Job Description AI/ML Engineer We are seeking a sharp, innovative, and passionate AI/ML Engineer to join our R&D team. You will be responsible for designing and deploying intelligent algorithms that enhance the predictive capabilities of our remote diagnostics platform used in railway signaling and other critical infrastructure. Responsibilities: Build and deploy machine learning and deep learning models for predictive maintenance and anomaly detection. Analyze time-series sensor data to develop accurate failure prediction models. Work with massive datasets – cleaning, preprocessing, feature engineering, and model tuning. Develop and expose ML models as REST APIs for integration into cloud-based systems. Collaborate with domain experts, data engineers, and full stack developers to create robust, production-grade solutions. Perform continuous evaluation and improvement of deployed models (MLOps). Stay updated with the latest AI research and evaluate them for real-world application. Required Skills & Qualifications: 2–5 years of experience in AI/ML project development and deployment. Proficiency in Python , Scikit-learn , Pandas , NumPy . Hands-on experience with TensorFlow or PyTorch . Solid understanding of ML algorithms , classification , regression , clustering , NLP , or computer vision . Experience with model deployment , APIs , and Docker . Knowledge of cloud platforms (AWS/GCP/Azure) is a plus. Strong problem-solving skills and understanding of statistical concepts. Nice to Have: Experience with time-series forecasting and anomaly detection. Prior exposure to predictive maintenance in industrial environments. Familiarity with IoT data and sensor networks.

Educational Requirements MCA,MSc,Bachelor of Engineering,BBA,BCom Service Line Data & Analytics Unit Responsibilities Technical knowledge- has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to Expertise in Object Oriented Python Programming with 4 -5 years experience. DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud Technologies Additional Responsibilities: Domain / Technical / Tools Knowledge: Object oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices and tools such a MLFlow, Kubernetes, etc. Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architecture understanding is a plus Technical and Professional Requirements: Education and Experience: Overall, 6 to 8 years of experience in Data driven software engineering with 3-5 years of experience designing, building and deploying enterprise AI or ML applications with at least 2 years of experience implementing full lifecycle ML automation using MLOps(scalable development to deployment of complex data science workflows) Bachelors or Masters degree in Computer Science Engineering or equivalent Domain experience in Retail, CPG and Logistics etc. Azure Certified DP100, AZ/AI900 Preferred Skills: Technology->Data Science->Machine Learning

Sr MLOps Engineer Experience: 5 - 7 Years Exp Salary : 15 to 20 LPA Preferred Notice Period: Within 30 Days Shift: 10:00AM to 7:00PM IST Opportunity Type: Hybrid (Ahmedabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : ML model deployment, MLOps, Monitoring Inferenz(One of Uplers' Clients) is Looking for: Sr MLOps Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description About Inferenz At Inferenz, our team of innovative technologists and domain experts help accelerating the business growth through digital enablement and navigating the industries with data, cloud and AI services and solutions. We dedicate our resources to increase efficiency and gain a greater competitive advantage by leveraging various next generation technologies. Our technology expertise has helped us delivering the innovative solutions in key industries such as Healthcare & Life Sciences, Consumer & Retail, Financial Services and Emerging industries. About The Role: Job Overview: Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. We are looking for a highly skilled MLOps Engineer to join our team. As an MLOps Engineer, you will be responsible for designing, implementing, and maintaining the infrastructure that supports the deployment, monitoring, and scaling of machine learning models in production. You will work closely with data scientists, software engineers, and DevOps teams to ensure seamless integration of machine learning models into our production systems. The job is NOT for you if: You dont want to build a career in AI/ML. Becoming an expert in this technology and staying current will require significant self-motivation. You like the comfort and predictability of working on the same problem or code base for years. The tools, best practices, architectures, and problems are all going through rapid change — you will be expected to learn new skills quickly and adapt. Key Responsibilities: Model Deployment: Design and implement scalable, reliable, and secure pipelines for deploying machine learning models to production. Infrastructure Management: Develop and maintain infrastructure as code (IaC) for managing cloud resources, compute environments, and data storage. Monitoring and Optimization: Implement monitoring tools to track the performance of models in production, identify issues, and optimize performance. Collaboration: Work closely with data scientists to understand model requirements and ensure models are production ready. Automation: Automate the end-to-end process of training, testing, deploying, and monitoring models. Continuous Integration/Continuous Deployment (CI/CD): Develop and maintain CI/CD pipelines for machine learning projects. Version Control: Implement model versioning to manage different iterations of machine learning models. Security and Governance: Ensure that the deployed models and data pipelines are secure and comply with industry regulations. Documentation: Create and maintain detailed documentation of all processes, tools, and infrastructure. Qualifications: 5+ years of experience in a similar role (DevOps, DataOps, MLOps, etc.) Bachelor’s or master’s degree in computer science, Engineering, or a related field. Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) Strong understanding of machine learning lifecycle, data pipelines, and model serving. Proficiency in programming languages such as Python, Shell scripting, and familiarity with ML frameworks (TensorFlow, PyTorch, etc.). Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.) Experience with CI/CD tools like Jenkins, GitLab CI, or similar Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent) Strong software engineering skills in complex, multi-language systems Comfort with Linux administration Experience working with cloud computing and database systems Experience building custom integrations between cloud-based systems using APIs Experience developing and maintaining ML systems built with open-source tools Experience developing with containers and Kubernetes in cloud computing environments Familiarity with one or more data-oriented workflow orchestration frameworks (MLFlow, KubeFlow, Airflow, Argo, etc.) Ability to translate business needs to technical requirements Strong understanding of software testing, benchmarking, and continuous integration Exposure to machine learning methodology and best practices Understanding of regulatory requirements for data privacy and model governance. Preferred Skills: Excellent problem-solving skills and ability to troubleshoot complex production issues. Strong communication skills and ability to collaborate with cross-functional teams. Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack). Knowledge of database systems (SQL, NoSQL). Experience with Generative AI frameworks Preferred cloud-based or MLOps/DevOps certification (AWS, GCP, or Azure) How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: At Inferenz, our team of innovative technologists and domain experts help accelerating the business growth through digital enablement and navigating the industries with data, cloud and AI services and solutions. We dedicate our resources to increase efficiency and gain a greater competitive advantage by leveraging various next generation technologies. Our technology expertise has helped us delivering the innovative solutions in key industries such as Healthcare & Life Sciences, Consumer & Retail, Financial Services and Emerging industries.. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Role & Responsibility Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. • Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. • Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. • Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. • Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. • Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. • Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. • Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. • Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. • Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. • Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. • Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. • Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. • Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. • Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture.

Whats in it for You Career Development: Build a meaningful career with a leading global company at the forefront of technology. Dynamic Work Environment: Work in an environment that is dynamic and forward-thinking, directly contributing to innovative solutions. Skill Enhancement: Enhance your software development skills on an enterprise-level platform. Versatile Experience: Gain full-stack experience and exposure to cloud technologies. Leadership Opportunities: Mentor peers and influence the products future as part of a skilled team. Work Flexibility: Benefit from a flexible work arrangement, balancing office time with the option to work from home. Community Engagement: Utilize five paid days for charity work or volunteering, supporting your passion for community service. Responsibilities: Design and implement cloud solutions using AWS and Azure. Develop and maintain Infrastructure as Code (IAC) with Terraform. Create and manage CI/CD pipelines using GitHub Actions and Azure DevOps. Automate deployment processes and provisioning of compute instances and storage. Orchestrate container deployments with Kubernetes. Develop automation scripts in Python, PowerShell, and Bash. Monitor and optimize cloud resources for performance and cost-efficiency using tools like Datadog and Splunk. Configure Security Groups, IAM policies, and roles in AWS\Azure. Troubleshoot production issues and ensure system reliability. Collaborate with development teams to integrate DevOps and MLOps practices. Create comprehensive documentation and provide technical guidance. Continuously evaluate and integrate new AWS services and technologies Cloud engineering certifications (AWS, Terraform) are a plus. Excellent communication and problem-solving skills. Minimum Qualifications: Bachelors Degree in Computer Science or equivalent experience. Minimum of 8+ years in cloud engineering, DevOps, or Site Reliability Engineering (SRE). Hands-on experience with AWS and Azure cloud services, including IAM, Compute, Storage, ELB, RDS, VPC, TGW, Route 53, ACM, Serverless computing, Containerization, CloudWatch, CloudTrail, SQS, and SNS. Experience with configuration management tools like Ansible, Chef, or Puppet. Proficiency in Infrastructure as Code (IAC) using Terraform. Strong background in CI/CD pipelines using GitHub Actions and Azure DevOps. Knowledge of MLOps or LLMops practices. Proficient in scripting languages: Python, PowerShell, Bash. Ability to work collaboratively in a fast-paced environment. Preferred Qualifications: Advanced degree in a technical field. Extensive experience with ReactJS and modern web technologies. Proven leadership in agile and project management. Advanced knowledge of CI/CD and industry best practices in software development.

Job Summary: We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production-ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state of the art machine learning systems that create value for the business. Responsibilities: Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback. Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. Optimization of the queries and pipelines. Modernization of the applications whenever required Qualifications: Expertise in programming languages like Python, SQL Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems. Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.) Understanding of model evaluation metrics, model monitoring tools and practices. Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. Bachelors Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: Experience in Azure MLOPS, Familiarity with Cloud Billing. Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. Experience working in an Agile environment, understanding of Lean Agile principles.

Position - Lead Machine Learning Engineer- MLOps, VertexAI, LLMs, GenAI, ML Model Management Role Overview UPS Data Science and Machine Learning team is seeking a highly skilled and experienced Lead Machine Learning Engineer to manage our AI, ML, GenAI application focused on Cross Border logistics. This position leverages continuous integration and deployment of the best practices, including test automation and monitoring, to ensure successful deployment of optimal ML models and analytical systems. You will be responsible for the end-to-end lifecycle of AI models, from experimentation and fine-tuning to deployment and management in production. A strong background in prompt engineering and practical experience with either Google Cloud's Vertex AI platform is essential for this role. You will also provide technical leadership and mentorship to other members of the AI/ML team. Key Responsibilities Lead the development and deployment of generative AI solutions utilizing LLMs, SLMs, and FMs for various applications (e.g., content generation, chatbots, summarization, code generation, etc.). Architect and implement robust and scalable infrastructure for training, fine-tuning, and serving large-scale AI models, leveraging either Vertex AI. Drive the fine-tuning and adaptation of pre-trained models using proprietary data to achieve state-of-the-art performance on specific tasks. Develop and implement effective prompt engineering strategies to elicit desired outputs and control the behavior of generative models. Manage the lifecycle of deployed models , production support, including monitoring performance, identifying areas for improvement, and implementing necessary updates or retraining. Collaborate closely with cross-functional teams (e.g., product, engineering, research) to understand business requirements and translate them into technical solutions. Provide technical leadership and mentorship to junior machine learning engineers, fostering a culture of learning and innovation. Ensure the responsible and ethical development and deployment of AI models , considering factors such as bias, fairness, and privacy. Stay up to date with latest advancements in generative AI, LLMs, and related technologies, and evaluate their potential application within the company. Document technical designs, implementation details, and deployment processes. Troubleshoot and resolve issues related to model performance and deployment. Required Skills and Experience: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Minimum of 5-8 years of hands-on experience in building, deploying, and managing machine learning models in a production environment. Demonstrable experience in managing, deploying, and fine-tuning large language models (LLMs), small language models (SLMs), and foundation models (FMs). Significant hands-on experience with prompt engineering techniques for various generative AI tasks. Proven experience working with either Google Cloud's Vertex AI platform platform . including experience with their respective model registries, deployment tools, and MLOps features. Strong programming skills in Python and experience with relevant machine learning libraries (e.g., TensorFlow, PyTorch, Transformers). Experience with cloud computing platforms (beyond Vertex AI is a plus, e.g. Azure). Solid understanding of machine learning principles, deep learning architectures, and evaluation metrics. Excellent problem-solving, analytical, and communication skills. Ability to work independently and as part of a collaborative team. Experience with MLOps practices and tools for continuous integration and continuous delivery (CI/CD) of ML models is highly desirable. Experience with version control systems (e.g., Git). Bonus Points: Experience with model governance frameworks and implementing ethical AI practices. Experience with specific generative AI use cases relevant to Logistics industry. Publications or contributions to open-source projects, technical blogs, or industry conferences are considered a plus Familiarity with data engineering pipelines and tools. Familiarity with emerging trends in generative AI, reinforcement learning from human feedback (RLHF), and federated learning approaches.

POSITION Senior Data Engineer / Data Engineer LOCATION Bangalore/Mumbai/Kolkata/Gurugram/Hyd/Pune/Chennai EXPERIENCE 2+ Years JOB TITLE: Senior Data Engineer / Data Engineer OVERVIEW OF THE ROLE: As a Data Engineer or Senior Data Engineer, you will be hands-on in architecting, building, and optimizing robust, efficient, and secure data pipelines and platforms that power business-critical analytics and applications. You will play a central role in the implementation and automation of scalable batch and streaming data workflows using modern big data and cloud technologies. Working within cross-functional teams, you will deliver well-engineered, high-quality code and data models, and drive best practices for data reliability, lineage, quality, and security. HASHEDIN BY DELOITTE 2025 Mandatory Skills: Hands-on software coding or scripting for minimum 3 years Experience in product management for at-least 2 years Stakeholder management experience for at-least 3 years Experience in one amongst GCP, AWS or Azure cloud platform Key Responsibilities: Design, build, and optimize scalable data pipelines and ETL/ELT workflows using Spark (Scala/Python), SQL, and orchestration tools (e.g., Apache Airflow, Prefect, Luigi). Implement efficient solutions for high-volume, batch, real-time streaming, and event-driven data processing, leveraging best-in-class patterns and frameworks. Build and maintain data warehouse and lakehouse architectures (e.g., Snowflake, Databricks, Delta Lake, BigQuery, Redshift) to support analytics, data science, and BI workloads. Develop, automate, and monitor Airflow DAGs/jobs on cloud or Kubernetes, following robust deployment and operational practices (CI/CD, containerization, infra-as-code). Write performant, production-grade SQL for complex data aggregation, transformation, and analytics tasks. Ensure data quality, consistency, and governance across the stack, implementing processes for validation, cleansing, anomaly detection, and reconciliation. Collaborate with Data Scientists, Analysts, and DevOps engineers to ingest, structure, and expose structured, semi-structured, and unstructured data for diverse use-cases. Contribute to data modeling, schema design, data partitioning strategies, and ensure adherence to best practices for performance and cost optimization. Implement, document, and extend data lineage, cataloging, and observability through tools such as AWS Glue, Azure Purview, Amundsen, or open-source technologies. Apply and enforce data security, privacy, and compliance requirements (e.g., access control, data masking, retention policies, GDPR/CCPA). Take ownership of end-to-end data pipeline lifecycle: design, development, code reviews, testing, deployment, operational monitoring, and maintenance/troubleshooting. Contribute to frameworks, reusable modules, and automation to improve development efficiency and maintainability of the codebase. Stay abreast of industry trends and emerging technologies, participating in code reviews, technical discussions, and peer mentoring as needed. Skills & Experience: Proficiency with Spark (Python or Scala), SQL, and data pipeline orchestration (Airflow, Prefect, Luigi, or similar). Experience with cloud data ecosystems (AWS, GCP, Azure) and cloud-native services for data processing (Glue, Dataflow, Dataproc, EMR, HDInsight, Synapse, etc.). © HASHEDIN BY DELOITTE 2025 Hands-on development skills in at least one programming language (Python, Scala, or Java preferred); solid knowledge of software engineering best practices (version control, testing, modularity). Deep understanding of batch and streaming architectures (Kafka, Kinesis, Pub/Sub, Flink, Structured Streaming, Spark Streaming). Expertise in data warehouse/lakehouse solutions (Snowflake, Databricks, Delta Lake, BigQuery, Redshift, Synapse) and storage formats (Parquet, ORC, Delta, Iceberg, Avro). Strong SQL development skills for ETL, analytics, and performance optimization. Familiarity with Kubernetes (K8s), containerization (Docker), and deploying data pipelines in distributed/cloud-native environments. Experience with data quality frameworks (Great Expectations, Deequ, or custom validation), monitoring/observability tools, and automated testing. Working knowledge of data modeling (star/snowflake, normalized, denormalized) and metadata/catalog management. Understanding of data security, privacy, and regulatory compliance (access management, PII masking, auditing, GDPR/CCPA/HIPAA). Familiarity with BI or visualization tools (PowerBI, Tableau, Looker, etc.) is an advantage but not core. Previous experience with data migrations, modernization, or refactoring legacy ETL processes to modern cloud architectures is a strong plus. Bonus: Exposure to open-source data tools (dbt, Delta Lake, Apache Iceberg, Amundsen, Great Expectations, etc.) and knowledge of DevOps/MLOps processes. Professional Attributes: Strong analytical and problem-solving skills; attention to detail and commitment to code quality and documentation. Ability to communicate technical designs and issues effectively with team members and stakeholders. Proven self-starter, fast learner, and collaborative team player who thrives in dynamic, fast-paced environments. Passion for mentoring, sharing knowledge, and raising the technical bar for data engineering practices. Desirable Experience: Contributions to open source data engineering/tools communities. Implementing data cataloging, stewardship, and data democratization initiatives. Hands-on work with DataOps/DevOps pipelines for code and data. Knowledge of ML pipeline integration (feature stores, model serving, lineage/monitoring integration) is beneficial. © HASHEDIN BY DELOITTE 2025 EDUCATIONAL QUALIFICATIONS: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field (or equivalent experience). Certifications in cloud platforms (AWS, GCP, Azure) and/or data engineering (AWS Data Analytics, GCP Data Engineer, Databricks). Experience working in an Agile environment with exposure to CI/CD, Git, Jira, Confluence, and code review processes. Prior work in highly regulated or large-scale enterprise data environments (finance, healthcare, or similar) is a plus.

Job Summary: We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production-ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state of the art machine learning systems that create value for the business. Responsibilities: Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback. Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. Optimization of the queries and pipelines. Modernization of the applications whenever required Qualifications: Expertise in programming languages like Python, SQL Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems. Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.) Understanding of model evaluation metrics, model monitoring tools and practices. Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. Bachelors Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: Experience in Azure MLOPS, Familiarity with Cloud Billing. Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. Experience working in an Agile environment, understanding of Lean Agile principles.

Key Responsibilities: 10+ years - Architect to design scalable, production-grade AI/ML systems and data pipelines. Collaborate with stakeholders to identify and scope use cases for machine learning, deep learning, NLP, and computer vision. Lead the selection and implementation of AI/ML frameworks, platforms, and tools (e.g., TensorFlow, PyTorch, Scikit-learn). Guide teams in model development, training, evaluation, deployment, and monitoring in production environments. Define MLOps strategy for CI/CD, model versioning, retraining, and governance. Ensure solutions are optimized for performance, scalability, security, and compliance. Work closely with data engineers to ensure robust data pipelines, feature stores, and data quality standards. Serve as an AI thought leader within the organization, mentoring engineers and promoting best practices. Evaluate new AI technologies and platforms to drive innovation and maintain competitive advantage. Contribute to documentation, standards, and architecture governance.

Provide technical leadership in the design, development, and maintenance of scalable build systems and deployment pipelines for AI/ML components, setting standards for quality, reliability, and performance. Mentor and guide a team of engineers, promoting best practices in C++, Python, CI/CD, and infrastructure automation. Design and implement robust build automation systems that support large, distributed AI/C++/Python codebases. Develop tools and scripts to enable developers and researchers to rapidly iterate, test, and deploy across diverse environments. Integrate C++ components with Python-based AI workflows, ensuring compatibility, performance, and maintainability. Lead the creation of portable, reproducible development environments, ensuring parity between development and production systems. Maintain and extend CI/CD pipelines for Linux and z/OS, applying best practices in automated testing, artifact management, and release validation. Collaborate with cross-functional teams including AI researchers, system architects, and mainframe engineers to align infrastructure with strategic and technical goals. Proactively monitor and improve build performance, automation coverage, and system reliability, identifying opportunities for innovation and optimization. Contribute to internal documentation, process improvements, and knowledge sharing to scale impact across teams and foster a culture of continuous improvement. Required education Bachelor's Degree Preferred education Bachelor's Degree Required technical and professional expertise Expert-level programming skills in C++ and Python, with a strong grasp of both compiled and interpreted language paradigms; able to provide architectural guidance and code-level mentorship. Demonstrated leadership in building and maintaining complex automation pipelines (CI/CD) using tools like Jenkins or GitLab CI, including the ability to define strategy, review team contributions, and drive implementation. In-depth experience with build tools and systems such as CMake, Make, Meson, or Ninja, including development of custom scripts and support for cross-compilation in heterogeneous environments. Proven experience leading multi-platform development efforts, particularly on Linux and IBM z/OS, with a deep understanding of platform-specific toolchains, constraints, and performance considerations. Expertise in integrating native C++ code with Python using tools like pybind11 or Cython, ensuring high-performance and maintainable interoperability across language boundaries. Strong diagnostic and debugging skills, with the ability to lead teams in resolving build-time, runtime, and integration issues in large-scale, multi-component systems. Proficiency in shell scripting (e.g., Bash, Zsh) and system-level operations, with the ability to coach others in scripting best practices. Familiarity with containerization technologies like Docker, and a track record of leading the adoption or optimization of container-based development and deployment workflows. Excellent communication and collaboration skills, with the ability to coordinate across disciplines, align technical efforts with strategic goals, and foster a high-performing engineering culture. Preferred technical and professional experience Working knowledge of AI/ML frameworks such as PyTorch, TensorFlow, or ONNX, with an understanding of how to integrate them into scalable, production-grade environments,able to guide teams in best practices for deployment and optimization. Experience developing or maintaining software on IBM z/OS mainframe systems, with the ability to mentor others in navigating legacy-modern hybrid ecosystems. Familiarity with z/OS build and packaging workflows, including leading efforts to streamline and modernize tooling where appropriate. Solid understanding of system performance tuning in high-throughput compute and I/O environments (e.g., large-scale model training or inference pipelines), and the ability to direct optimization strategies. Knowledge of GPU computing and low-level profiling/debugging tools, with experience driving performance-critical initiatives. Experience managing long-lifecycle enterprise systems, ensuring forward- and backward-compatibility across releases and deployments through proactive planning and versioning strategies. Background contributing to or maintaining open-source projects in infrastructure, DevOps, or AI tooling domains, with a focus on community engagement and sustainability. Proficiency in distributed systems, microservice architectures, and REST APIs, including guiding architectural decisions that balance performance, maintainability, and scalability. Proven experience leading integration of MLOps pipelines with CI/CD frameworks, ensuring seamless, secure, and automated deployment of AI/ML models into production workflows. Exceptional communication and stakeholder management skills, capable of clearly articulating technical strategies and trade-offs to non-technical audiences. Demonstrated ability to foster collaboration and alignment across diverse, cross-functional teams, including AI researchers, DevOps engineers, and enterprise architects. Track record of ensuring compliance with industry standards, security policies, and best practices in enterprise-scale AI engineering. Commitment to maintaining high standards of code quality, performance, and security, with the ability to review and enforce standards across a team or organization.