58 Ml Deployment Jobs

Responsibilities: Lead and manage end-to-end data science projects, including project planning, scoping, budgeting, resource allocation, and timeline management. Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to define project requirements, deliverables, and milestones. Conduct project kick-off meetings, define project objectives, and establish clear project scopes and deliverables. Develop and maintain project plans, track progress, and provide regular updates to stakeholders, ensuring project milestones are met within the defined timeline. Identify and manage project risks, issues, and dependencies, implementing mitigation strategies and contingency plans as necessary. Coordinate and communicate project activities, ensuring alignment with organizational goals and objectives. Monitor project budget, resource utilization, and expenses, making necessary adjustments to optimize project performance and efficiency. Foster a collaborative and innovative work environment, promoting knowledge sharing and best practices within the data science team. Stay updated with industry trends, emerging technologies, and advancements in data science methodologies, and propose relevant process improvements or tool adoption. Provide leadership and mentorship to the data science team, facilitating their professional growth and development. Requirements: Bachelor's degree in computer science, Data Science, Statistics, or a related field. A master's degree is preferred. Proven experience (5+ years) in managing data science projects, preferably in a fast-paced and dynamic environment. Strong understanding of data science concepts, machine learning algorithms, and statistical modeling techniques. Proficient in project management methodologies, tools, and techniques. Excellent organizational and time management skills, with the ability to prioritize tasks and meet deadlines. Exceptional problem-solving and analytical abilities, with keen attention to detail. Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and present complex concepts to non-technical stakeholders. Strong leadership skills, with the ability to motivate and inspire team members. Experience with data visualization tools, programming languages (e. g., Python, R), and big data technologies is a plus. Whatspp - 9886683329

Job Title: Senior Data Scientist Intelligent Offer Management Location: Bangalore (3 days work from office) Type: Hybrid Full-time (On-Site) Experience Level: 3–6 years About the Role: We are hiring a Data Scientist to lead and scale our Intelligent Offer Management engine – a key pillar of our revenue optimization strategy. This role sits at the intersection of data science, pricing strategy, product thinking, and experimentation, enabling personalized, dynamic, and contextual offer decisions at scale. You'll work closely with product managers, business teams, and engineers to build models that recommend the right offer to the right customer at the right time. Key Responsibilities: Design and implement real-time offer recommendation models using historical purchase, engagement, and behavioral data. Develop algorithms for dynamic pricing, offer personalisation, segmentation, and discount optimisation. Collaborate with cross-functional stakeholders to translate business requirements into data science solutions. Conduct A/B and multivariate experiments to evaluate offer performance and optimise offer strategies. Build predictive models for conversion propensity, elasticity curves, and uplift measurement. Package models for production deployment, ensuring scalability, performance, and monitoring. Influence product design and customer journeys with data-driven insights. Must-Have Qualifications: 3-6 years of experience in data science, quantitative analytics, or pricing strategy roles. Proficiency in Python and SQL, with experience in building, validating, and deploying ML models. Deep understanding of offer science, pricing elasticity, behavioral economics, and incentive systems. Strong business acumen with the ability to balance accuracy with impact and speed. Experience working with experimentation frameworks and statistical inference. Ability to communicate complex insights effectively to both technical and non-technical stakeholders.

Role & responsibilities Build and deploy production-grade ML pipelines for varied use cases across operations, manufacturing, supply chain, and more Work hands-on in designing, training, and fine-tuning models across traditional ML, deep learning, and GenAI (LLMs, diffusion models, etc.) Collaborate with data scientists to transform exploratory notebooks into scalable, maintainable, and monitored deployments Implement CI/CD pipelines, version control, and experiment tracking using tools like MLflow, DVC, or similar Do shadow deployment and A/B testing of production models Partner with data engineers to build data pipelines that support real-time or batch model inference Ensure high availability, performance, and observability of deployed ML solutions using MLOps best practices Conduct code reviews, performance tuning, and contribute to ML infrastructure improvements Support the end-to-end lifecycle of ML products Contribute to knowledge sharing, reusable component development, and internal upskilling initiatives Preferred candidate profile Bachelor's in Computer Science, Engineering, Data Science, or related field. Masters degree preferred 4–6 years of experience in developing and deploying machine learning models, with significant exposure to MLOps practices Experience in implementing and productionizing Generative AI applications using LLMs (e.g., OpenAI, HuggingFace, LangChain, RAG architectures) Strong programming skills in Python; familiarity with ML libraries such as scikit-learn, TensorFlow, PyTorch Hands-on experience with tools like MLflow, Docker, Kubernetes, FastAPI/Flask, Airflow, Git, and cloud platforms (Azure/AWS) Solid understanding of software engineering fundamentals and DevOps/MLOps workflows Exposure to at least 2-3 industry domains (energy, manufacturing, finance, etc.) preferred Excellent problem-solving skills, ownership mindset, and ability to work in agile cross-functional teams

Hands-on experience of deployment of models on Snowflake, and writing stored procedures for the same, OR Hands-on experience with Cortex AI and its frameworks. Experience in doing indexing, parsing, & Document AI within Cortex. Required Candidate profile Experience in Snowflake data platform and infrastructure Snow-park should be known, Data Science theoretical or DS Candidates should be only from Tamil Nadu

About the job : Zeta Global is looking for an experienced ML Engineer with industry-proven hands-on experience of delivering machine learning models to production to solve business problems. To be a good fit to join our AI/ML team, you should ideally: Be a thought leader that can work with cross-functional partners to foster a data-driven organization. Be a strong team player, have experience contributing to a large project as part of a collaborative team effort. Have extensive knowledge and expertise with machine learning best-practices and industry standards. Be able to design and execute experiments to measure the impact of new features and machine learning models on business outcomes. Empower the product and engineering teams to make data-driven decisions. What you need to succeed 46 years of proven, hands-on industry experience as a Machine Learning Engineer. Strong command of Python and SQL; experience with distributed computing frameworks like Spark or Hadoop is a significant plus. A bachelors degree in computer science, Mathematics, Physics, Statistics, Operations Research, or a related field is preferred. Solid experience with ML infrastructureincluding model deployment, evaluation, data processing, and debuggingwith a track record of building scalable ML solutions from business requirements. Proficient in Applied LLMs and NLP, with experience developing solutions using frameworks such as Lang Chain, Lang Smith, and backend tools like Fast API. Hands-on experience with LLMs like GPT or Claude is highly desirable. Strong background in building, deploying, and integrating AI agents using LLMs is a big plus. Comfortable with software engineering best practices, including version control, unit/integration testing, and setting up CI/CD pipelines for reliable ML deployment. Committed to high code quality, with an emphasis on thorough code reviews and testing. Excellent communication skills, with the ability to collaborate across technical and non-technical teams and clearly present outcomes to business stakeholders. Our technology runs on the Zeta Marketing Platform, which powers end to end marketing programs for some of the worlds leading brands. With expertise encompassing all digital marketing channels Email, Display, Social, Search and Mobile Zeta orchestrates acquisition and engagement programs that deliver results that are scalable, repeatable and sustainable.

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options 3. Team Management a. Talent Management i. Support on boarding and training to enhance capability & effectiveness Mandatory Skills: Data Analysis. Experience: 3-5 Years.

Build predictive & GenAI models (LLM chatbots, RAG pipelines) Deploy models using MLOps practices on cloud (AWS/GCP/Azure) Design A/B tests and run impact-focused data experimentsEnsure data quality, governance,

About the Role: Grade Level (for internal use): 09 The Team As a member of the EDO, Collection Platforms & AI Cognitive Engineering team you will build and maintain enterprisescale data extraction, automation, and ML model deployment pipelines that power data sourcing and information retrieval solutions for S&P Global. You will learn to design resilient, production-ready systems in an AWS-based ecosystem while leading by example in a highly engaging, global environment that encourages thoughtful risk-taking and self-initiative. Whats in it for you: Be part of a global company and deliver solutions at enterprise scale Collaborate with a hands-on, technically strong team (including leadership) Solve high-complexity, high-impact problems end-to-end Build, test, deploy, and maintain production-ready pipelines from ideation through deployment Responsibilities: Develop, deploy, and operate data extraction and automation pipelines in production Integrate and deploy machine learning models into those pipelines (e.g., inference services, batch scoring) Lead critical stages of the data engineering lifecycle, including: End-to-end delivery of complex extraction, transformation, and ML deployment projects Scaling and replicating pipelines on AWS (EKS, ECS, Lambda, S3, RDS) Designing and managing DataOps processes, including Celery/Redis task queues and Airflow orchestration Implementing robust CI/CD pipelines on Azure DevOps (build, test, deployment, rollback) Writing and maintaining comprehensive unit, integration, and end-to-end tests (pytest, coverage) Strengthen data quality, reliability, and observability through logging, metrics, and automated alerts Define and evolve platform standards and best practices for code, testing, and deployment Document architecture, processes, and runbooks to ensure reproducibility and smooth hand-offs Partner closely with data scientists, ML engineers, and product teams to align on requirements, SLAs, and delivery timelines Technical Requirements: Expert proficiency in Python, including building extraction libraries and RESTful APIs Hands-on experience with task queues and orchestrationCelery, Redis, Airflow Strong AWS expertiseEKS/ECS, Lambda, S3, RDS/DynamoDB, IAM, CloudWatch Containerization and orchestration Proven experience deploying ML models to production (e.g., SageMaker, ECS, Lambda endpoints) Proficient in writing tests (unit, integration, load) and enforcing high coverage Solid understanding of CI/CD practices and hands-on experience with Azure DevOps pipelines Familiarity with SQL and NoSQL stores for extracted data (e.g., PostgreSQL, MongoDB) Strong debugging, performance tuning, and automation skills Openness to evaluate and adopt emerging tools and languages as needed Good to have: Master's or Bachelor's degree in Computer Science, Engineering, or related field 2-6 years of relevant experience in data engineering, automation, or ML deployment Prior contributions on GitHub, technical blogs, or open-source projects Basic familiarity with GenAI model integration (calling LLM or embedding APIs) Whats In It For You Our Purpose: Progress is not a self-starter. It requires a catalyst to be set in motion. Information, imagination, people, technologythe right combination can unlock possibility and change the world.Our world is in transition and getting more complex by the day. We push past expected observations and seek out new levels of understanding so that we can help companies, governments and individuals make an impact on tomorrow. At S&P Global we transform data into Essential Intelligence, pinpointing risks and opening possibilities. We Accelerate Progress. Our People: Our Values: Integrity, Discovery, Partnership At S&P Global, we focus on Powering Global Markets. Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals. Benefits: We take care of you, so you cantake care of business. We care about our people. Thats why we provide everything youand your careerneed to thrive at S&P Global. Health & WellnessHealth care coverage designed for the mind and body. Continuous LearningAccess a wealth of resources to grow your career and learn valuable new skills. Invest in Your FutureSecure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs. Family Friendly PerksIts not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families. Beyond the BasicsFrom retail discounts to referral incentive awardssmall perks can make a big difference. For more information on benefits by country visithttps://spgbenefits.com/benefit-summaries Global Hiring and Opportunity at S&P Global: At S&P Global, we are committed to fostering a connected andengaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets. Recruitment Fraud Alert If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, pre-employment training or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here. ---- Equal Opportunity Employer S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment. If you need an accommodation during the application process due to a disability, please send an email to EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person. US Candidates Only The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf ---- IFTECH202.1 - Middle Professional Tier I (EEO Job Group)

About Position: We are conducting an in-person hiring drive for the position of GenAI Engineer in Pune & Bengaluru on 19th July 2025. Interview Location is mentioned below: Pune: Persistent Systems, Veda Complex, Rigveda-Yajurveda-Samaveda-Atharvaveda Plot No. 39, Phase I, Rajiv Gandhi Information Technology Park, Hinjawadi, Pune, 411057 Bangalore: Persistent Systems, The Cube at Karle Town Center Rd, DadaMastan Layout, Manayata Tech Park, Nagavara, Bengaluru, Karnataka 560024 The ideal candidate will have a strong background in working with GEN AI , ML ,LangChain, LangGraph, Microservices (Fast API), Prompt engineering. You will work closely with our data analysts, engineers, and business teams to ensure optimal performance, scalability, and availability of our data pipelines and analytics. Role: GenAI Engineer Job Location: All PSL Locations Experience: 4 Years-12 Years Job Type: Full Time Employment What You'll Do: Develop and fine-tune LLMs for real-world use cases Build and optimize RAG pipelines using Langchain or similar tools Integrate GenAI capabilities into enterprise applications Hands on Python coding exp Work with vector databases and embedding techniques Collaborate with cross-functional teams on AI-driven features Were looking for a skilled engineer with hands-on experience in LLMs, LangChain, and chatbot development. Expertise You'll Bring: 4+ years of experience working with data science. Strong Python skills and familiarity with TensorFlow, PyTorch, and scikit-learn are essential Basic understanding of Flask, Django, or FastAPI is a plus. Strong analytical, problem-solving, and troubleshooting skills. Ability to work independently and in a collaborative team environment. Cloud deployment knowledge on AWS, Azure, or GCP is a must. Youll work on real-world GenAI applications in a collaborative, fast-paced environment. Stay updated with the latest in GenAI and contribute to innovative solutions. Benefits: Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company-sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards Annual health check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Inclusive Environment: Persistent Ltd. is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We offer hybrid work options and flexible working hours to accommodate various needs and preferences. Our office is equipped with accessible facilities, including adjustable workstations, ergonomic chairs, and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment. We are committed to creating an inclusive environment where all employees can thrive. Our company fosters a values-driven and people-centric work environment that enables our employees to: Accelerate growth, both professionally and personally Impact the world in powerful, positive ways, using the latest technologies Enjoy collaborative innovation, with diversity and work-life wellbeing at the core Unlock global opportunities to work and learn with the industry’' best Let's unleash your full potential at Persistent "Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Mandatory Skills: Python for Data Science Experience: 8-10 Years

The role of this position is to define, architect, and lead the delivery of machine learning and AI solutions. You will be responsible for demand generation through support in solution development, collaborating with sales, pre-sales, and consulting teams to create solutions and propositions for proactive demand generation. Additionally, you will contribute to the development of solutions and proof of concepts aligned with key offerings to enable solution-led sales. You will also collaborate with different colleges and institutes for recruitment, joint research initiatives, and provide data science courses. Furthermore, you will focus on revenue generation by building and operationalizing Machine Learning and Deep Learning solutions. This will involve developing machine learning/deep learning models for decision augmentation or automation solutions, collaborating with ML Engineers, Data Engineers, and IT to evaluate ML deployment options, and integrating model performance management tools into the current business infrastructure. In terms of team management, you will be involved in resourcing by supporting the recruitment process to onboard the right resources for the team. You will also handle talent management by supporting onboarding and training for team members to enhance capability and effectiveness, managing team attrition, conducting timely performance reviews, providing constructive feedback to direct reports, being a role model to the team, and ensuring that the Performance Nxt framework is followed for the entire team. Additionally, you will lead and drive engagement initiatives for the team. The key performance parameters for this role include demand generation measured by order bookings, revenue generation through delivery assessed based on timeliness, customer success stories, and customer use cases, and capability building and team management evaluated by the percentage of the team trained on new skills and team attrition percentage.,

Hiring a Senior GenAI Engineer with 7-12 years of experience in Python, Machine Learning, and Large Language Models (LLMs) for a 6-month engagement based in Gurugram This hands-on role involves building intelligent systems using Langchain and RAG, developing agent workflows, and defining technical roadmaps The ideal candidate will be proficient in LLM architecture, prompt engineering, vector databases, and cloud platforms (AWS, Azure, GCP) The position demands strong collaboration skills, a system design mindset, and a focus on production-grade AI/ML solutions

We are looking for candidates with 7 + years of experience in below skills - Primary skills : Cloud -google cloud preferred but cloud any will do ML deployment using Jenkins/Harness Python Kubernetes terraform

AI in Cloud Deployment, combining AI/ML expertise with cloud infrastructure and DevOps skills Job Title: Devops Engineer Location: Chennai Job Type: Full-Time Job Summary: We are looking for a highly skilled AI Cloud Deployment Engineer to join our team to build, deploy, and scale AI/ML models and pipelines in cloud environments. The ideal candidate has strong experience with cloud platforms (Azure, AWS, GCP), containerization, CI/CD, and MLOps best practices. You will work closely with data scientists, ML engineers, and DevOps teams to operationalize AI solutions. Key Responsibilities: Design and implement cloud-based deployment pipelines for AI/ML models using Azure, AWS. Collaborate with data scientists to containerize and deploy ML models using Docker and Kubernetes. Set up CI/CD pipelines for continuous model integration and deployment (MLOps). Monitor, troubleshoot, and optimize deployed AI services for performance and reliability. Manage scalable data pipelines and APIs using cloud-native technologies. Apply security best practices to model deployment and API access control. Automate infrastructure provisioning with tools like Terraform or ARM templates. Ensure reproducibility and versioning of ML experiments using tools like MLflow, DVC, or SageMaker. Build dashboards and logging/monitoring systems for AI applications. Required Skills : Hands-on experience in AI/ML model deployment in cloud environments (Azure ML, SageMaker, Vertex AI, etc.). Proficiency in cloud computing: AWS, Azure, or GCP. Experience with Docker, Kubernetes, and container orchestration. Strong understanding of MLOps , including model versioning, drift detection, and pipeline automation. Familiarity with CI/CD tools: GitHub Actions, Jenkins, GitLab CI, or Azure DevOps. Good programming skills in Python (FastAPI for serving models),Java . Experience with infrastructure-as-code (Terraform, CloudFormation, Azure Bicep). Knowledge of cloud security, networking, and identity management. Preferred Qualifications: Bachelors or Masters degree in Computer Science, Data Science, or related field. Experience with ML tools like MLflow, Kubeflow, SageMaker Pipelines. Knowledge of data engineering frameworks (Airflow, Kafka, Spark). Prior experience working in Agile/Scrum environments. Nice to Have: Exposure to edge deployment for AI models (e.g., on IoT devices). Knowledge of LLMOps and deploying generative AI models in production. Familiarity with serverless compute services (AWS Lambda, Azure Functions).

Job Description If you are looking for a career at a dynamic company with a people-first mindset and a deep culture of growth and autonomy, ACV is the right place for you! Competitive compensation packages and learning and development opportunities, ACV has what you need to advance to the next level in your career. We will continue to raise the bar every day by investing in our people and technology to help our customers succeed. We hire people who share our passion, bring innovative ideas to the table, and enjoy a collaborative atmosphere. Who we are: ACV is a technology company that has revolutionized how dealers buy and sell cars online. We are transforming the automotive industry. ACV Auctions Inc. (ACV), has applied innovation and user-designed, data driven applications and solutions. We are building the most trusted and efficient digital marketplace with datasolutions for sourcing, selling and managing used vehicles with transparency and comprehensive insights that were once unimaginable. We are disruptors of theindustry and we want you to join us on our journey. ACVs network of brands includes ACV Auctions, ACV Transportation, ClearCar, MAX Digital and ACV Capital within its Marketplace Products, as well as, True360 and Data Services. ACV Auctions is opening its new India Development Center in Chennai, India, and were looking for talented individuals to join our team. As we expand our platform, were offering a wide range of exciting opportunities across various roles. At ACV, we put people first and believe in the principles of trust and transparency. If you are looking for an opportunity to work with the best minds in the industry and solve unique business and technology problems? Look no further! Join us in shaping the future of the automotive marketplace! At ACV we focus on the Health, Physical, Financial, Social and Emotional Wellness of our Teammates and to support this we offer industry leading benefits and wellness programs. What you will do: ACVs Machine Learning (ML) team is looking to grow its MLOps team. Multiple ACV operations and product teams rely on the ML teams solutions. Current deployments drive opportunities in the marketplace, in operations, and sales, to name a few. As ACV has experienced hyper growth over the past few years, the volume, variety, and velocity of these deployments has grown considerably. Thus, the training, deployment, and monitoring needs of the ML team has grown as weve gained traction. MLOps is a critical function to help ourselves continue to deliver value to our partners and our customers. Successful candidates will demonstrate excellent skill and maturity, be self-motivated as well as team-oriented, and have the ability to support the development and implementation of end-to-end ML-enabled software solutions to meet the needs of their stakeholders. Those who will excel in this role will be those who listen with an ear to the overarching goal, not just the immediate concern that started the query. They will be able to show their recommendations are contextually grounded in an understanding of the practical problem, the data, and theory as well as what product and software solutions are feasible and desirable. The core responsibilities of this role are: Working with fellow machine learning engineers to build, automate, deploy, and monitor ML applications. Developing data pipelines that feed ML models. Deploy new ML models into production. Building REST APIs to serve ML models predictions. Monitoring performance of models in production. Required Qualifications: Graduate education in a computationally intensive domain or equivalent work experience. 5+ years of prior relevant work or lab experience in ML projects/research, and 1+ years as tech lead or major contributor on cross-team projects. Advanced proficiency with Python, SQL etc. Experience with cloud services (AWS / GCP) and kubernetes, docker, CI/CD. Preferred Qualifications: Experience with MLOps-specific tooling like Vertex AI, Ray, Feast, Kubeflow, or ClearML, etc. are a plus. Experience with EDA, including data pipeline building and data visualization. Experience with building ML models.

National Payment Corporation of India (NPCI) is hiring Data Scientist (with Deep Learning Experience) Job Title: Data Scientist (Deep Learning Strong experience) Experience: 2 Years to 8 Years MUST HAVE SKILLS REQUIRED: (STRONG) Programming & DH : Python, Data Structures, Algorithms (STRONG) Data Handling : Python, EDA, Statistics, Probability Distributions (STRONG) Learning & Deep Learning : TensorFlow, PyTorch, GANs, Transformers (STRONG) Cloud & GPU: GCP, GPU-based machines, Model Optimization (STRONG) Model Lifecycle : Custom Loss Functions, Evaluation Metrics, Scalability If you find yourself Perfectly fit based on above skillset criteria. Please share your Updated resume at vinayak.ojha_ori@npci.org.in Overview: NPCI Data Analytics team is seeking a talented and motivated Data Scientist to join our team. As a Data Scientist, you will play a crucial role in modelling complex problems, uncovering insights, and identifying new opportunities through the use of statistical, algorithmic, mining, and visualization techniques. You will be focused on utilizing NPCI product data and analytics to solve business problems by applying machine learning and deep learning techniques, conducting statistical analysis, and building high-quality predictive systems that are integrated with our services. Key Responsibilities: Develop and implement deep learning models for various applications. Work with large datasets and perform exploratory data analysis. Develop custom loss functions and evaluation metrics. Collaborate with cross-functional teams to ensure successful deployment of models. Optimize models for performance, scalability, and efficiency. Conduct research and stay up to date with the latest developments in the field of deep learning. Participate in code reviews and provide feedback to team members. Qualifications: Bachelor's or Master's degree in Computer Science, Mathematics, or related field with 3+ years of experience. Strong proficiency in Python programming language. Good understanding of data structures and algorithms. Strong understanding of statistics and probability distributions Hands-on Experience with GANs and Transformers Experience with both Tensorflow and Pytorch frameworks. Familiarity with Google Cloud Platform (GCP) and working on GPU based machines. Strong problem-solving skills. Excellent written and verbal communication skills. Ability to work independently as well as in a team environment. Strong Communication skills and ability to simplify complex information and results into simple concepts and actionable insights for a variety of audiences.

We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) or LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems. You will work on automating ML workflows, optimizing model deployment, and managing large-scale AI applications, including LLMs (Large Language Models) , ensuring they run efficiently in production. Key Responsibilities: Design and implement end-to-end MLOps pipelines for training, validation, deployment, monitoring, and retraining of ML models. Optimize and fine-tune large language models (LLMs) for various applications, ensuring performance and efficiency. Develop CI/CD pipelines for ML models to automate deployment and monitoring in production. Monitor model performance, detect drift , and implement automated retraining mechanisms. Work with cloud platforms ( AWS, GCP, Azure ) and containerization technologies ( Docker, Kubernetes ) for scalable deployments. Implement best practices in data engineering , feature stores, and model versioning. Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications. Ensure compliance with security, privacy, and ethical AI standards in ML deployments. Optimize inference performance and cost of LLMs using quantization, pruning, and distillation techniques . Deploy LLM-based APIs and services, integrating them with real-time and batch processing pipelines. Key Requirements: Technical Skills: Strong programming skills in Python, with experience in ML frameworks ( TensorFlow, PyTorch, Hugging Face, JAX ). Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker, Airflow). Deep understanding of LLM architectures , prompt engineering, and fine-tuning. Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools . Proficiency in cloud services (AWS/GCP/Azure) for ML model training and deployment. Experience with monitoring ML models (Prometheus, Grafana, Evidently AI). Knowledge of feature stores (Feast, Tecton) and data pipelines (Kafka, Apache Beam). Strong background in distributed computing (Spark, Ray, Dask) . Soft Skills: Strong problem-solving and debugging skills. Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders. Passion for staying updated with the latest ML and LLM research & technologies . Preferred Qualifications: Experience with LLM fine-tuning , Reinforcement Learning with Human Feedback ( RLHF ), or LoRA/PEFT techniques . Knowledge of vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented generation ( RAG ). Familiarity with LangChain, LlamaIndex , and other LLMOps-specific frameworks. Experience deploying LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc.) .

About the Role: Grade Level (for internal use): 09 The Team As a member of the EDO, Collection Platforms & AI Cognitive Engineering team you will build and maintain enterprisescale data extraction, automation, and ML model deployment pipelines that power data sourcing and information retrieval solutions for S&P Global. You will learn to design resilient, production-ready systems in an AWS-based ecosystem while leading by example in a highly engaging, global environment that encourages thoughtful risk-taking and self-initiative. Whats in it for you: Be part of a global company and deliver solutions at enterprise scale Collaborate with a hands-on, technically strong team (including leadership) Solve high-complexity, high-impact problems end-to-end Build, test, deploy, and maintain production-ready pipelines from ideation through deployment Responsibilities: Develop, deploy, and operate data extraction and automation pipelines in production Integrate and deploy machine learning models into those pipelines (e.g., inference services, batch scoring) Lead critical stages of the data engineering lifecycle, including: End-to-end delivery of complex extraction, transformation, and ML deployment projects Scaling and replicating pipelines on AWS (EKS, ECS, Lambda, S3, RDS) Designing and managing DataOps processes, including Celery/Redis task queues and Airflow orchestration Implementing robust CI/CD pipelines on Azure DevOps (build, test, deployment, rollback) Writing and maintaining comprehensive unit, integration, and end-to-end tests (pytest, coverage) Strengthen data quality, reliability, and observability through logging, metrics, and automated alerts Define and evolve platform standards and best practices for code, testing, and deployment Document architecture, processes, and runbooks to ensure reproducibility and smooth hand-offs Partner closely with data scientists, ML engineers, and product teams to align on requirements, SLAs, and delivery timelines Technical : Expert proficiency in Python, including building extraction libraries and RESTful APIs Hands-on experience with task queues and orchestrationCelery, Redis, Airflow Strong AWS expertiseEKS/ECS, Lambda, S3, RDS/DynamoDB, IAM, CloudWatch Containerization and orchestrationDocker (mandatory), basic Kubernetes (preferred) Proven experience deploying ML models to production (e.g., SageMaker, ECS, Lambda endpoints) Proficient in writing tests (unit, integration, load) and enforcing high coverage Solid understanding of CI/CD practices and hands-on experience with Azure DevOps pipelines Familiarity with SQL and NoSQL stores for extracted data (e.g., PostgreSQL, MongoDB) Strong debugging, performance tuning, and automation skills Openness to evaluate and adopt emerging tools and languages as needed Good to have: Master's or Bachelor's degree in Computer Science, Engineering, or related field 2-6 years of relevant experience in data engineering, automation, or ML deployment Prior contributions on GitHub, technical blogs, or open-source projects Basic familiarity with GenAI model integration (calling LLM or embedding APIs) Whats In It For You Our Purpose: Progress is not a self-starter. It requires a catalyst to be set in motion. Information, imagination, people, technologythe right combination can unlock possibility and change the world.Our world is in transition and getting more complex by the day. We push past expected observations and seek out new levels of understanding so that we can help companies, governments and individuals make an impact on tomorrow. At S&P Global we transform data into Essential Intelligence, pinpointing risks and opening possibilities. We Accelerate Progress. Our People: Our Values: Integrity, Discovery, Partnership At S&P Global, we focus on Powering Global Markets. Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals. Benefits: We take care of you, so you cantake care of business. We care about our people. Thats why we provide everything youand your careerneed to thrive at S&P Global. Health & WellnessHealth care coverage designed for the mind and body. Continuous LearningAccess a wealth of resources to grow your career and learn valuable new skills. Invest in Your FutureSecure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs. Family Friendly PerksIts not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families. Beyond the BasicsFrom retail discounts to referral incentive awardssmall perks can make a big difference. For more information on benefits by country visithttps://spgbenefits.com/benefit-summaries Global Hiring and Opportunity at S&P Global: At S&P Global, we are committed to fostering a connected andengaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets. Recruitment Fraud Alert If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, pre-employment training or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here. ----------------------------------------------------------- Equal Opportunity Employer S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment. If you need an accommodation during the application process due to a disability, please send an email to EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person. US Candidates Only The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf ----------------------------------------------------------- IFTECH202.1 - Middle Professional Tier I (EEO Job Group)

Hiring a Senior GenAI Engineer with 712 years of experience in Python, Machine Learning, and Large Language Models (LLMs) for a 6-month engagement based in Gurugram. This hands-on role involves building intelligent systems using Langchain and RAG, developing agent workflows, and defining technical roadmaps. The ideal candidate will be proficient in LLM architecture, prompt engineering, vector databases, and cloud platforms (AWS, Azure, GCP). The position demands strong collaboration skills, a system design mindset, and a focus on production-grade AI/ML solutions.

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Deliver No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition % Mandatory Skills: AI Cognitive. Experience5-8 Years.

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Deliver No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition % Competencies Client Centricity Passion for Results Collaborative Working Problem Solving & Decision Making Effective communicationMandatory Skills: Python for Data Science. Experience8-10 Years.

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options 3. Team Management a. Talent Management i. Support on boarding and training to enhance capability & effectiveness Deliver No. Performance Parameter Measure 1. Demand generation # PoC supported 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management # Skills acquired Mandatory Skills: Data Science. Experience3-5 Years.

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Deliver No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition % Mandatory Skills: Data Science. Experience5-8 Years.

RESPONSIBILITIES: Develop, productionize, and deploy scalable, resilient software solutions for operationalizing AI & ML. Deploy Machine Learning (ML) models and Large Language Models (LLM) securely and efficiently, both in the cloud and on-premises, using state of the art platforms, tools, and techniques. Provide effective model observability, monitoring, and metrics by instrumenting logging, dashboards, alerts, etc. In collaboration with Data Engineers, design and build pipelines for extraction, transformation, and loading of data from a variety of data sources for AI & ML models as well as RAG architectures for LLMs. Enable Data Scientists to work more efficiently by providing tools for experiment tracking and test automation. Ensure scalability of built solutions by developing and running rigorous load tests. Facilitate integration of AI & ML capabilities into user experience by building APIs, UIs, etc. Stay current on new developments in AI & ML frameworks, tools, techniques, and architectures available for solution development, both private and open source. Coach data scientists and data engineers on software development best practices to write scalable, maintainable, well-designed code. Agile Project Work Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions. Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions. Advocate for security and responsibility best practices and tools. Acts as an expert on complex technical topics that require cross-functional consultation. Perform other duties as required. QUALIFICATIONS: Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development Experience with popular MLOps tools (e.g., Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.) Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.) Experience in programming languages common to data science such as Python, SQL, etc. Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.). Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC. Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc. Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value. Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking. Ability to communicate complex ideas in a concise way. Fluent with popular diagraming and presentation software. Demonstrated experience in teaching and/or mentoring professionals.

We are looking for an experienced Machine Learning Engineer to lead the development and deployment of ML models that power intelligent products and insights. You will collaborate with teams across Data Science, Engineering, and Product to build solutions that are scalable, efficient, and impactful. Candidates with exposure to the healthcare domain are encouraged to apply, although this is not a mandatory requirement . Key Responsibilities: Architect, build, and maintain end-to-end ML systems from data pipelines to model deployment. Develop and optimize machine learning models for use cases such as classification, prediction, recommendation, NLP, or computer vision. Implement MLOps best practices for model training, tracking, deployment, and monitoring. Collaborate with data scientists and domain experts to productionize prototypes and research. Evaluate and monitor model performance; ensure robustness, fairness, and explainability. Document architecture and processes; contribute to knowledge sharing and code reviews. (Preferred) Work with EHR data, claims data, clinical notes, or healthcare interoperability formats like HL7 or FHIR, if applicable. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related discipline. 5+ years of hands-on experience in ML engineering or applied data science. Strong command of Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). Experience deploying ML models in production using containerization (Docker, Kubernetes) and cloud platforms (AWS/GCP/Azure). Familiarity with MLOps tools like MLflow, DVC, or Kubeflow. Proficient in building ETL/ELT pipelines and handling large-scale datasets. Strong understanding of statistical methods, model evaluation metrics, and optimization techniques. Good software engineering practices (version control, testing, CI/CD). Preferred Qualifications: Exposure to healthcare datasets such as medical claims, EHR/EMR, HL7, FHIR, or medical coding (CPT, ICD-10). Experience with NLP models applied to clinical documentation or unstructured medical data. Understanding of HIPAA compliance, data anonymization, and PHI handling. Contributions to open-source ML projects or peer-reviewed publications. Experience working in regulated industries or mission-critical environments.