35 Ml Pipelines Jobs

Responsibilities: Design and develop AI/ML models Process large dataset Build Python pipeline or React UIs deploy models as APIs or UI features Collaborate with teams, test models, present results Maintain clear documentation of tools and workflows

Role: Data Science Leader Experience: 10-15 years of overall experience, with at least 5+ years in Data science roles along with 24 years in a Leadership or Managerial capacity. Technical Skills: Data Science & Machine Learning Deep understanding of statistical modeling, predictive analytics, clustering, NLP, and time series forecasting. Strong grasp of model evaluation, fairness, explainability, and business alignment. Programming Proficient in Python (or R) with experience in data science libraries like pandas, NumPy, scikit-learn, TensorFlow, or PyTorch. Ability to read and review code, debug, and advise on best practices. Cloud Computing Working knowledge of at least one major cloud platform (AWS, GCP, Azure). Experience with cloud-native tools for data storage (S3, BigQuery), compute (EC2, GKE, Lambda), and ML services (SageMaker, Vertex AI, Azure ML). Data Engineering Fundamentals Understanding of data pipelines, ETL/ELT processes. Familiarity with tools like Airflow, dbt, Spark, SQL. Data Visualization Experience with dashboards and reporting tools (Tableau, Power BI, Looker, or custom visualizations using Plotly/Altair). Skilled in transforming complex outputs into clear, compelling narratives for non-technical stakeholders. MLOps / Deployment Familiarity with modern ML development and deployment practices. Basic understanding of deploying models to production, CI/CD pipelines, and monitoring. Generative AI Familiarity with GenAI concepts, including large language models (LLMs), embeddings, prompt engineering, and RAG pipelines. Familiarity with tools and APIs like OpenAI, Hugging Face, LangChain. Leadership & People Management Team Management Experience leading and mentoring teams of 510 individuals across varying levels. Proven track record of building and scaling Data science teams, delivering impactful projects Conduct performance reviews, manage career growth, and foster a healthy team culture. Cross-Functional Collaboration Proven ability to work closely with product, engineering, marketing, and business teams. Hiring & Talent Development Skilled in identifying top talent, conducting interviews, onboarding, and team capability building. Project & Stakeholder Management Project Management Experienced in managing multiple projects simultaneously. Comfortable with Agile methodologies, sprint planning, and delivery tracking. Stakeholder Communication Translating technical insights into business terms. Communicates technical insights clearly to senior executives. Problem Solving & Scope Management Ability to break down ambiguous business problems into solvable components. Define scope and ensure projects align with business impact. Strategic Thinking Aligns team objectives with company vision and business goals. Drives roadmap planning, and long-term capability building. Business Acumen Domain Knowledge Deep understanding of any one business vertical among the following e.g., telecom, BFSI, fintech, e-commerce, healthcare etc. Impact Orientation Focus on delivering measurable business value from data science efforts. Strong grasp of metrics, KPIs, and ROI-driven thinking. Strategic Thinking Ability to align data science efforts with long-term business goals. Soft Skills Exceptional communication, both verbal and written. Decision-Making: Balanced between data, intuition, team input and strategic vision. Empathy & Emotional Intelligence: Especially for managing team dynamics and motivation. Adaptability: In the face of changing priorities or business goals and organizational change.

This is an urgent and fast filling position - Need immediate joiners OR less than 1 month notice period AI/ML Engineer Location: Chennai Job Summary: We are looking for a Senior AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Role & responsibilities Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification. Reinforcement Learning: Knowledge of reinforcement learning concepts and techniques like Q-learning and policy gradients.

Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment Collaborate with data scientists and software engineers to operationalize ML models Develop and maintain CI/CD pipelines for ML workflows Implement monitoring and logging solutions for ML models Optimize ML infrastructure for performance, scalability, and cost-efficiency Ensure compliance with data privacy and security regulations Strong programming skills in Python, with experience in ML frameworks Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes) Proficiency in cloud platform (AWS) and their ML-specific services Experience with MLOps tools Experience with ML model serving frameworks (TensorFlow Serving, TorchServe) Primary Skills Machine Learning CI/CD Pipelines Devops Secondary Skills Good Communication

Roles and Responsibilities: • Design, develop, and deploy machine learning models to solve real-world business problems. • Develop production-quality machine learning pipelines and frameworks using Python and Java. • Collaborate with data scientists, engineers, and business teams to define data requirements, model specifications, and system architecture. • Preprocess and clean large datasets, perform feature engineering, and ensure data quality for ML applications. • Implement, optimize, and deploy algorithms, ensuring scalability and performance in production systems. • Work with cloud-based platforms (AWS, GCP, Azure) for deploying and scaling machine learning models. • Stay current with the latest machine learning research and trends, and propose innovative solutions and techniques. • Document processes, models, and code to ensure reproducibility, maintainability, and knowledge sharing. Required Skills & Qualifications: • 5-6 years of professional experience in software engineering, with at least 2-3 years of hands-on experience in machine learning and data science. • Proficiency in Python for machine learning, data analysis, and algorithm development. • Fair experience with Java, including the development of production-level applications and integration with ML models. • Familiarity with data preprocessing techniques, including feature extraction, cleaning, normalization, and transformation. • Strong experience with SQL and working with large datasets from databases and data lakes. • Proficiency in cloud platforms (AWS, GCP, or Azure) for deploying machine learning models and managing infrastructure. • Strong knowledge of version control systems like Git and experience with CI/CD pipelines for machine learning models. • Strong problem-solving and debugging skills. • Ability to work independently and in a collaborative team environment. • Excellent communication skills for technical documentation and presenting solutions to stakeholders. Desired Skills: • Familiarity with big data technologies such as Spark, or Kafka. • Experience with DevOps practices for automating ML workflows. • Experience in deploying ML models using Docker, Kubernetes, or similar containerization technologies.

We are Hiring Senior Python with Machine Learning Engineer Level 3 for a US based IT Company based in Hyderabad. Candidates with minimum 7 Years of experience in python and machine learning can apply. Job Title : Senior Python with Machine Learning Engineer Level 3 Location : Hyderabad Experience : 7+ Years CTC : 28 LPA - 30 LPA Working shift : Day shift Job Description: We are seeking a highly skilled and experienced Python Developer with a strong background in Machine Learning (ML) to join our advanced analytics team. In this Level 3 role, you will be responsible for designing, building, and deploying robust ML pipelines and solutions across real-time, batch, event-driven, and edge computing environments. The ideal candidate will have extensive hands-on experience in developing and deploying ML workflows using AWS SageMaker , building scalable APIs, and integrating ML models into production systems. This role also requires a strong grasp of the complete ML lifecycle and DevOps practices specific to ML projects. Key Responsibilities: Develop and deploy end-to-end ML pipelines for real-time, batch, event-triggered, and edge environments using Python Utilize AWS SageMaker to build, train, deploy, and monitor ML models using SageMaker Pipelines, MLflow, and Feature Store Build and maintain RESTful APIs for ML model serving using FastAPI , Flask , or Django Work with popular ML frameworks and tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Ensure best practices across the ML lifecycle: data preprocessing, model training, validation, deployment, and monitoring Implement CI/CD pipelines tailored for ML workflows using tools like Bitbucket , Jenkins , Nexus , and AUTOSYS Design and maintain ETL workflows for ML pipelines using PySpark , Kafka , AWS EMR , and serverless architectures Collaborate with cross-functional teams to align ML solutions with business objectives and deliver impactful results Required Skills & Experience: 5+ years of hands-on experience with Python for scripting and ML workflow development 4+ years of experience with AWS SageMaker for deploying ML models and pipelines 3+ years of API development experience using FastAPI , Flask , or Django 3+ years of experience with ML tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Strong understanding of the complete ML lifecycle: from model development to production monitoring Experience implementing CI/CD for ML using Bitbucket , Jenkins , Nexus , and AUTOSYS Proficient in building ETL processes for ML workflows using PySpark , Kafka , and AWS EMR Nice to Have: Experience with H2O.ai for advanced machine learning capabilities Familiarity with containerization using Docker and orchestration using Kubernetes For further assistance contact/whatsapp : 9354909517 or write to hema@gist.org.in

We seek an innovative professional with expertise in AI tools like Copilot to boost engineering productivity. The role involves identifying bottlenecks, proposing tailored GenAI solutions, and integrating them into networking product development to streamline workflows and drive AI adoption across teams. Technologies: ------------------- GenAI Algorithms, LLM's, NLP, Hugging Face, RAG, OpenAI models, Microsoft and GitHub Copilot, Agentic AI, AI-powered tools, Advanced python for Datascience, Flash , REST API, debugging tools, parser tools, troubleshooting and investigating capabilities. Required Qualifications: ------------------------ 2+ years using GitHub Copilot, OpenAI, and other AI coding tools to boost development productivity. 2+ years in GenAImodel design, fine-tuning, and deployment. 3+ years in Pythonbuilding AI/ML pipelines, automation tools, and data processing scripts. Hands-on experience applying GenAI/ML for code generation, summarization, workflow automation, and virtual assistants. Delivered AI-driven impact in 23 engineering projects. Ability to identify and develop use cases in BI, automation, and areas like smart troubleshooting and adaptive testing. Proficient in LLMs, NLP, and AI/ML for software and networking applications. Knowledge of integrating AI with platforms like Webex, Zoom, and Teams via APIs. Skilled in scripting, automation, and AI-augmented workflows. Experienced in Agile/Scrum. Strong problem-solving, communication, and mentoring skills; able to work independently and collaboratively. Desired Skills: ------------------ GitHub Copilot certification. Experience with networking products software development. Familiarity with CI/CD, DevOps, and automation tools. Understanding of responsible AI practices. Experience with data analysis and visualization for AI productivity. Knowledge of cloud-based AI services and integrations. Primary Skills GenAI, LLMs, AI Tools, Python

We are seeking a highly skilled AI Engineer with demonstrated expertise in both traditional Machine Learning modelling and modern GenAI techniques. This role involves building scalable AI systems using foundation models, parameter- efficient fine-tuning (PEFT), and robust MLOps pipelines, while also grounding your solutions in classical machine learning principles. The ideal candidate excels at blending structured data modelling with large language model capabilities to drive intelligent, efficient, and production-ready AI systems. Key Responsibilities: Design, build, and deploy predictive systems by combining traditional ML models (e.g., regression, tree-based, time series) with Generative AI capabilities (e.g., LLMs, embeddings, GenAI-based simulation). Strong skills in prompt engineering are required to support the development and deployment of generative AI solutions. Fine-tune foundation models using PEFT techniques (e.g., LoRA, prefix tuning, adapters) for domain-specific applications. Build and maintain end-to-end MLOps pipelines for model versioning, testing, deployment, monitoring, and retraining. Use LLMs for automating feature generation, synthetic data creation, and prompt-driven scenario modelling. Manage the full AI/ML model lifecycle: experimentation, deployment, monitoring, diagnostics, and iterative enhancements. Apply and validate classical ML algorithms (e.g., XGBoost, LightGBM, ARIMA, SVMs, etc.) as benchmarks or hybrid components. Collaborate with cross-functional teams (data scientists, ML engineers, DevOps, product) to deliver AI solutions aligned with business needs. Deploy and monitor solutions in cloud environments (e.g., AWS SageMaker, GCP Vertex AI, Azure ML, REST(FAST API) with automated retraining and model health checks. Required Skills and Qualifications: Bachelors or Master’s degree in Computer Science, Machine Learning, Data Science, or related field. 3+ years of experience in AI/ML engineering, with proven success in both traditional predictive modelling and LLM-based development. Strong Python programming skills and familiarity with ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, and Transformers. Experience in parameter-efficient fine-tuning (PEFT) using frameworks such as Hugging Face PEFT, QLoRA, or AdapterHub. Proficiency in building MLOps workflows with tools like MLflow, Airflow, Kubeflow, or SageMaker Pipelines. Solid SQL/NoSQL experience and strong data wrangling skills. Hands-on experience deploying models in production cloud environments and containerized systems (e.g., Docker, Kubernetes). Excellent problem-solving, debugging, and communication skills.

Job Summary We are seeking an AI Leader for building Guardrail Platform to drive the design, deployment, and governance of AI guardrails that ensure ethical, responsible, and compliant AI operations . This role involves collaborating with cross-functional teams to implement AI fairness, explainability, bias mitigation, security, and regulatory compliance frameworks across AI/ML pipelines. Roles & Responsibilities AI Guardrail Strategy & Implementation Define and implement AI guardrails to ensure ethical AI development, risk mitigation, and compliance. Establish automated monitoring for AI fairness, bias detection, and explainability. Lead the operationalization of Responsible AI (RAI) principles across the organization. AI Risk & Compliance Management Align AI models with regulatory standards (e.g., GDPR, AI Act, CCPA, NIST AI RMF). Develop governance frameworks for model validation, auditing, and risk assessment . Collaborate with legal, compliance, and security teams to ensure AI transparency. AI Model Security & Reliability Implement guardrails against adversarial attacks, data poisoning, and model drift . Establish secure AI deployment standards to prevent unauthorized AI model access or misuse. Establish DevSecOps pipeline teams to integrate AI security best practices . Operationalization & AI Governance Define AI monitoring KPIs for continuous risk assessment and compliance tracking. Develop automated pipelines to flag high-risk AI behaviors and decision anomalies. Foster a culture of explainable AI (XAI) & transparency for AI-driven decision-making. Cross-functional Leadership & Innovation Partner with product, legal, and engineering teams to integrate AI guardrails into MLOps workflows . Stay ahead of AI regulatory trends, industry best practices, and emerging risks . Competencies Required Skills Education Skills (NOT TO BE USED)

Job Overview (Primary Skills - GCP, Kubeflow, Python, Vertex.ai) As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with data science leadership and stakeholders to understand business objectives, map the scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that the client aspire to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision.

This is an urgent and fast filling position - Need immediate joiners OR less than 1 month notice period Senior AI/ML Engineer Location: Chennai Experience: 7+ years Job Summary: We are looking for a Senior AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Role & responsibilities Preferred candidate profile Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R.

MLOps Engineer Must-Have Skills Strong proficiency in Python/PySpark, especially in analytical contexts DevOps and CI/CD expertise with Git proficiency Ability to build and maintain CI/CD pipelines Code refactoring skills and knowledge of software development principles (OOPs, SOLID) Unit testing and exception handling expertise Deep understanding of ML techniques and their applications End-to-end ML lifecycle experience Experience with model deployment and cloud services Model monitoring and drift detection capabilities Knowledge to develop production-ready code implementations Knowledge of unsupervised & unsupervised learning techniques (Regression, Classification, clustering, etc) SQL proficiency Good To Have Skills Experience in the CPG/Retail domain Cloud computing and big data technologies expertise Experience with containerization and deployment of ML models Familiarity with MLOps-specific tools Experience with data visualization and data imputation techniques Background in software development practices and agile methodologies Experience automating model testing and validation processes Soft Skills Strong communication skills to collaborate with data scientists, data engineers, and architects Problem-solving abilities and analytical thinking Attention to detail, especially for audit-ready code Ability to work in cross-functional teams Technical documentation capabilities

Job Description : We are seeking a highly motivated and experienced ML Engineer/Data Scientist to join our growing ML/GenAI team. You will play a key role in designing, developing and productionalizing ML applications by evaluating models, training and/or fine tuning them. You will play a crucial role in developing Gen AI based solutions for our customers. As a senior member of the team, you will take ownership of projects, collaborating with engineers and stakeholders to ensure successful project delivery. What we're looking for: At least 3 years of experience in designing & building AI applications for customer and deploying them into production At least 5 years of Software engineering experience in building Secure, scalable and performant applications for customers. Experience with Document extraction using AI, Conversational AI, Vision AI, NLP or Gen AI. Design, develop, and operationalize existing ML models by fine tuning, personalizing it. Evaluate machine learning models and perform necessary tuning. Develop prompts that instruct LLM to generate relevant and accurate responses. Collaborate with data scientists and engineers to analyze and preprocess datasets for prompt development, including data cleaning, transformation, and augmentation. Conduct thorough analysis to evaluate LLM responses, iteratively modify prompts to improve LLM performance. Hands on customer experience with RAG solution or fine tuning of LLM model. Build and deploy scalable machine learning pipelines on GCP or any equivalent cloud platform involving data warehouses, machine learning platforms, dashboards or CRM tools. Experience working with the end-to-end steps involving but not limited to data cleaning, exploratory data analysis, dealing outliers, handling imbalances, analyzing data distributions (univariate, bivariate, multivariate), transforming numerical and categorical data into features, feature selection, model selection, model training and deployment. Proven experience building and deploying machine learning models in production environments for real life applications Good understanding of natural language processing, computer vision or other deep learning techniques. Expertise in Python, Numpy, Pandas and various ML libraries (e.g., XGboost, TensorFlow, PyTorch, Scikit-learn, LangChain). Familiarity with Google Cloud or any other Cloud Platform and its machine learning services. Excellent communication, collaboration, and problem-solving skills.

Position Summary: NPS Prism has experienced tremendous growth as a standalone software and data business over the past few years and is making the leap from being a consulting-led business to a technology-led business. Given that shift, we are looking to build our team with world-class team members to help drive business growth to its full potential in this next phase. This is a fantastic opportunity to help build the largest startup owned by Bain & Company and take NPS Prism into the future. Job Description: We are seeking an experienced and highly motivated Engineer III DevOps specializing in CI/CD, cloud technologies like Microsoft Azure / AWS, Docker, Kubernetes, Jenkins, python scripting, bash scripting, creating pipeline for data engineering and MLOps to join our dynamic team. The ideal candidate will have a strong background in the above technology stack to solve real-world problems and create highly scalable applications in our context. Key Responsibilities: Implement and manage continuous delivery systems and methodologies on AWS and Azure. Develop and maintain an efficient operations process that includes metrics reporting and platform performance. Implement best practices for automation, GitOps, and monitoring. Ensure the scalability, performance, and resilience of our system. Prioritize requests from operations and development teams fairly while demonstrating a sense of empathy. Leverage Jenkins for deployment of applications to the cloud environment. Enhance network security and performance through effective management of protocols, VPNs, VPCs, load balancing, and more. Lead the design and implementation of build, release, deployment, and configuration activities. Architect, set up, and maintain our infrastructure and development environments. Lead the design and implementation of CI/CD pipelines and DevOps practices. Oversee on-call management, troubleshooting, and system administration tasks. Manage and maintain tools to automate operational processes. Lead the daily maintenance and troubleshooting of the companys existing cloud infrastructure. Collaborate with development teams to ensure smooth and secure application integration and deployment. Work closely with cross-functional teams to identify and implement best practices and improvements in DevOps processes. Collaborate with developers to make sure new environments meet requirements and conform to best practices. Qualifications: Education: Bachelor's degree in computer science, information technology, or a related field. Experience: 5-9 years of overall experience with 2-3 years as Lead DevOps Engineer role or similar software engineering role. Experience with startup environments is highly desirable. Proficiency in using DevOps tools like Jenkins, Azure DevOps and GitHub Actions for continuous integration tool. Strong experience with cloud services such as AWS and Azure, including managing sensitive assets. Excellent knowledge of scripting languages such as Python, Bash, etc. Solid understanding of networking, storage, and virtualization. Skills: Strong problem-solving skills and ability to manage complex systems with minimal supervision. Excellent verbal and written communication skills in English language. Ability to work in a fast-paced environment and manage multiple tasks simultaneously. Preferred Skills: Experience in MLOps

Job Description : Strong understating of Python, ML concepts and frameworks, Fast API, Graph QL Experience in developing scalable APIs. Knowledge of AWS, preferred services are storage, EC2, Kubernetes Exposure of ML best practices, documentation and unit testing. ML Flow, AirFlow, ML pipeline creation, drift monitoring and control Experience in developing and deploying machine learning models in a production environment using CI/CD. Communicate with clients to understand requirements and ask right questions. Knowledge of Django and database design will be added advantage. Strong analytical and problem-solving skills. Experience in proprietary and public LLMs, including finetuning, inference optimization Experience in latest LLM frameworks/libraries (like LangChain, Langfuse, Ragas, Llamaindex, Huggingface, Chainlit)

Experience Required: 8+Years Mode of work: Remote Skills Required: DataBricks, building AI Agents using Azure Copilot Studio or Mosaic AI Agent Framework , Python,Azure and Databricks AI/ML services,developing AI/ML framework with Mosaic or open-source ML Notice Period : Immediate Joiners Primary Responsibilities: Develop and improve the AI agentic frameworks that allow AI agents to work together to achieve complex tasks. Develop novel data collection, fine-tuning, and AI technologies that achieve optimal performance on specific tasks and domains. Design and implement ML pipelines for data preprocessing , feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration. Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions Build scalable, reusable backend systems to support AI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance. Requirements: Hands-on programming experience with at least one modern language such as Python, Scala Experience contributing to the architecture and design of large-scale distributed systems and/or ML systems and tools Experience applying software engineering methodologies and best practices including coding standards, code reviews, build processes, testing, and security. Experience building AI Agents using Azure Copilot Studio or Mosaic AI Agent Framework Strong in Azure and Databricks AI/ML services with experience building AI applications Prior experience in developing AI/ML framework with Mosaic or open-source ML software is an advantage. Interested candidate can share your resume to OR you can refer your friend to Pavithra.tr@enabledata.com for the quick response.

Position Overview: As an AI Specialist at our Company, you will play a key role in leveraging artificial intelligence and machine learning to support product design, sourcing strategies, sales analysis, financial reporting, and budget planning. You will collaborate with cross-functional teams to ensure that AI solutions are effectively integrated into our workflows, driving efficiency and innovation. Key Responsibilities: 1. Product Design & Development: Utilize AI tools to analyze market trends, customer preferences, and performance data to assist in the design of innovative sports products. Develop predictive models for product success based on historical data and future trends. Collaborate with the product design team to optimize product features using AI-driven insights. 2. Sourcing & Supply Chain Optimization: Implement AI algorithms to optimize sourcing strategies by analyzing supplier performance, pricing trends, and material availability. Develop predictive models for demand forecasting to streamline inventory management and reduce costs. Assist in automating supplier selection processes based on predefined criteria using machine learning models. 3. Sales Analysis & Forecasting: Use AI models to analyze past sales data and predict future sales trends. Provide insights into customer behavior patterns and purchasing trends to inform marketing strategies. Collaborate with the sales team to optimize pricing strategies using AI-driven recommendations. 4. Financial Reporting & Budgeting: Develop AI-driven financial models for accurate forecasting and budgeting processes. Automate financial reporting tasks by integrating AI tools into existing systems. Analyze financial data to identify cost-saving opportunities and improve profitability across the business. 5. Cross-Functional Collaboration: Work closely with product development, finance, sales, and supply chain teams to understand their needs and deliver AI solutions that enhance decision-making processes. Provide training and support to teams on how to effectively use AI tools for their specific functions. Required Qualifications: Bachelors or Master’s degree in Data Science, Computer Science, Artificial Intelligence, or related field. Proven experience (5 to 8 years) working with AI/ML technologies in a business environment. Strong knowledge of machine learning algorithms, neural networks, natural language processing (NLP), and computer vision. Experience with programming languages such as Python, R, or JavaScript. Proficiency with AI platforms such as TensorFlow, PyTorch, or similar tools. Familiarity with data visualization tools like Power BI or Tableau. Experience in applying AI solutions within the sports industry or related fields is a plus. Desired Skills: Strong analytical skills with the ability to interpret complex datasets. Excellent problem-solving abilities and attention to detail. Ability to work collaboratively across departments while managing multiple projects simultaneously. Strong communication skills with the ability to present technical information clearly to non-technical stakeholders. What We Offer: Competitive salary based on experience. Opportunity to work in a fast-paced and innovative environment within the sports industry. Professional development opportunities including training in advanced AI technologies. A collaborative team culture that values creativity and innovation.

What you will do: 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 internal 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: Leading a team of machine learning engineers to build, automate, and deploy ML models and pipelines in production. Enact ML best practices for the team to follow. Developing ML model pipelines from proof of concept to production. Documenting the work process, including pipeline set up, experiment execution, and model deployment. Monitoring performance of models in production. Collaborate with stakeholders to help meet their objectives and provide them with periodic status updates. What you will need: Graduate education in a computationally intensive domain. 5+ years of prior relevant work or lab experience in ML projects, and 2+ years as tech lead or team management experience. Experience with building data pipelines (Kubeflow, sklearn pipelines, etc.). Experience with building and deploying REST APIs (Flask, FastAPI) Advanced proficiency with Python and SQL (BigQuery/MySQL). Extensive knowledge of ML frameworks and libraries. (Ray, Feast, ClearML, etc.) Experience with cloud services (AWS / GCP), Docker, Kubernetes, and CI/CD pipelines. Monitor models performance and microservices health status in Production.

Exciting Opportunity Alert! Are you ready to take your career to the next level? Join a prestigious MNC in Pune as an AI/ML Engineer! Experience: 7-10 Years Position: AI/ML Engineer Mode: Full-Time Hybrid Location: Pune Notice Period: 0 to 30 Days Total Experience: With over 7 years in designing and developing end-to-end ML pipelines, including a minimum of 5 years working on large-scale ML projects. Data Science & Analytics: You should bring at least 5 years of expertise in data analytics, statistical programming, and data mining to tackle intricate challenges. Domain Knowledge: Having familiarity with Banking and AML would be a valuable asset. Technical Expertise: Demonstrate a strong grasp of data science methodologies, machine learning algorithms, and tools essential for constructing scalable production solutions. ML Frameworks & MLOps Tools: Proficiency in TensorFlow, PyTorch, MLflow, or Kubeflow is key. CI/CD & Big Data Systems: Experience with CI/CD tools and a solid foundation in big data platforms like Hadoop, Spark, and Hive. Bonus: Any experience with Dataiku would be a definite plus. Keen on this opportunity? Get in touch with us at: Email: shalini.v@saranshinc.com LinkedIn: linkedin.com/in/shalini-vodapelly-129560280 Ready to embrace this exciting career opportunity? hashtag#AI hashtag#ML hashtag#Engineer hashtag#Pune hashtag#DataScience hashtag#Analytics

Role & responsibilities Develop MLOps pipelines for AI/ML model training, validation, and deployment. Automate data ingestion, preprocessing, and feature engineering workflows. Optimize model training and inference performance for scalability and efficiency. Manage CI/CD pipelines for ML models using tools like Jenkins, CI/CD. Deploy models using cloud-based (AWS, Azure, GCP) and on-prem solutions (Docker, Kubernetes). Monitor ML models in production to ensure performance, reliability, and drift detection. Implement robust logging, version control, and rollback strategies for ML models. Work closely with data scientists, AI engineers, and Dev Ops teams to improve AI system integration. Ensure compliance with data security, governance, and model explain-ability best practices. Preferred candidate profile Perks and benefits

Key Responsibilities: CI/CD Pipeline Management & Automation: Design, implement, and maintain robust CI/CD pipelines for deploying machine learning models and solutions. Automate and streamline deployment processes using AWS services such as CodePipeline, CodeBuild, CodeDeploy, and CodeCommit. Ensure seamless integration of model training, testing, and deployment stages within the CI/CD pipeline. Set up and manage infrastructure as code (IaC) using tools like AWS CloudFormation or Terraform for creating scalable and reliable environments for ML applications. Automate deployment, scaling, and monitoring of machine learning models in AWS environments using AWS Lambda, ECS, EKS, and SageMaker. AWS Cloud Services Management & Security: Manage and configure AWS cloud services such as EC2, S3, SageMaker, Lambda, and others to support machine learning pipelines and production environments. Use AWS SageMaker for managing the ML lifecycle, including data preparation, training, tuning, and model deployment. Set up automated workflows for model retraining and versioning based on new data inputs and performance metrics. Ensure compliance with industry standards and internal policies regarding data privacy, security, and governance for machine learning solutions. Implement best practices in DevOps, including version control, code quality checks, and deployment automation using AWS services. Continuously improve infrastructure by staying up-to-date with new AWS features, best practices, and emerging technologies. Monitoring & Optimization: Monitor the performance of deployed ML models and pipelines using AWS CloudWatch, CloudTrail, and other monitoring tools. Implement automated testing, validation, and monitoring processes to ensure models perform as expected in production environments. Optimize costs and performance by automating resource scaling, ensuring high availability, and improving pipeline efficiency. Collaboration & Support: Collaborate with data scientists, machine learning engineers, and DevOps teams to integrate ML models into production systems. Provide support and troubleshooting expertise for pipeline issues, including model failures, deployment bottlenecks, and scaling problems. Work closely with security teams to implement best practices for security and compliance, ensuring that data and models are protected within AWS. Key Skills & Qualifications: Education: Bachelors degree in Computer Science, Information Technology, or a related field. Experience : 5+ years of experience as an AWS Engineer, DevOps Engineer, or Cloud Engineer, with a focus on CI/CD pipelines and machine learning solutions. Strong expertise in AWS cloud services (S3, EC2, SageMaker, Lambda, CodePipeline, etc.). Experience with CI/CD tools like AWS CodePipeline, GitLab CI, or similar platforms. Proficiency with containerization tools such as Docker and Kubernetes for managing microservices architecture. Knowledge of infrastructure as code (IaC) tools such as AWS CloudFormation, Terraform etc. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and deployment in production environments. Experience in setting up and managing CI/CD pipelines for machine learning or data science solutions. Familiarity with version control systems like Git and deployment automation practices. Strong knowledge of monitoring and logging tools (e.g., AWS CloudWatch) for real-time performance tracking. Ability to work collaboratively with cross-functional teams, including data scientists, ML engineers, and DevOps teams. Strong verbal and written communication skills for documentation and knowledge sharing.

Total AI Systems, Inc., headquartered in Lansing, Michigan, specializes in developing commercial software solutions that utilize advanced artificial intelligence to improve business efficiency. We are a profitable, product-based company with offices in the United States, India, and Canada. Our team is made up of rockstars who know that delivering a rockstar performance for our customers means going above and beyond. Whether its early mornings, late nights, or continuous practice and refinement, we are dedicated to providing our very best and having a lot of fun along the way. We take care of our employees, and in turn, they take care of us. We're seeking individuals who are ready to go the extra mile and join us on this journey! We are currently seeking AL/ML Engineers to join our dynamic team. The ideal candidate will possess deep technical expertise in machine learning and artificial intelligence, with a proven track record of developing scalable AI solutions. Your role will involve everything from data analysis and model building to integration and deployment, ensuring our AI initiatives drive substantial business impact. The following are some of the responsibilities for this position: Develop end-to-end AI/ML solutions with a strong focus on GenAI, NLP and predictive analytics. Identifying problems in the current product and working in tandem with AI and backend engineers or the implementation of the solutions to these problems and train/optimize/validate models for all the identified ML/AI/NLP/CV/GenAI problems. Experience delivering and maintaining productionized end-to-end Machine Learning solutions, form data preparation, experimentation, model training, model serving and model update. Design, train, and deploy ML models to production environments. Collaborate with developers, designers and product managers to build scalable and effective solutions. Write clean, maintainable and efficient code in Python and Node.js. Work with various databases (SQL, NoSQL, RDB, etc.) and ensure data integrity. Design and develop backend logic to support ML applications. Leverage cloud platforms (preferably AWS) for deploying and managing AI solutions. Use Docker to containerize applications for streamlined deployment. Continuously explore and integrate new ML tools, frameworks and best practices. This is an exciting position with a growing company and offers a lot of upsides. This is a challenging position that requires dedication and determination. Some requirements of the position include: 1-3 years of experience in machine learning engineering or AI, preferably in a SaaS environment. Hands on experience in NLP and generative AI models, with a solid foundation in Python, node js and exposure in model fine-tuning and training. Experience working in a small, product-based company with a hands-on approach. Proven track record of building and delivering software products in a small team environment. Solid understanding of databases (SQL, NoSQL, Relational Database, Low-code, No-code) and data modeling. Experience deploying ML models and services in production environments. Should have experience with NLP, GenAI technologies and cloud platforms (AWS preferred). Working knowledge of Docker for application deployment. Strong software engineering skills with an interest in end-to-end development. Passion for learning and exploring new technologies. Good to have familiarity with PHP.

6-15 years proven experience as a Data Scientist or Machine Learning Engineer Bachelors or Masters degree in Computer Science, Engineering or Data Science Machine Learning relevant field; graduate degree in Data Science or another quantitative field is preferred Key Skills Machine Learning Engineer with strong knowledge of Python and cloud based deployments Strong knowledge of EDA, NLP, Topic Modeling, NER, Embedding models and Semantic Search based techniques Have working knowledge of LLMs via APIs like Open AI and deploying open source LLMs like LLAMA2 and FLAN T5 etc Strong knowledge of Lang Chain framework and Fast API of python is a must have. Knowledge of runtimes like ONNX, Quantization, QLORA technique Working experience of Cloud preferably Azure. Understanding of MLOps frameworks is a big plus Knowledge of Classification, Q&A network using BERT etc Strong knowledge of tensorflow and pytorch frameworks

What Youll Do Work on ZS AI Products or client AI solutions using ML Engineering & Data Science Tech stack Work on creating GenAI applications such as answering engines, extraction components, and content authoring Provides technical expertise including the evaluation of different products in ML Tech stack Collaborate with data scientists to create state-of-the-art AI models Design and build ML Engineering platforms and components Design and build advanced ML pipelines for Feature engineering, inferencing and continuous model training Design and build ML Ops framework and components for model visibility and tracking Manage the complete ML lifecycle Leads team to achieve established goals, such as delivering new features or functionality Mentor and groom technical talent within the team Review individual work plans before implementation to identify potential issue areas and/or reduce rework Performs design / code reviews of the team to identify issues/risks and ensure robustness Drive estimation of technical components and tracks team's progress Handle client interactions as and when required Recommend designs that are scalable, testable, debuggable, robust, maintainable and usable Maintain a culture of rapid learning and explorations to drive innovations / POCs on niche technologies and architecture patterns; Systematically debug code issues using stack traces, logs, monitoring tools and other resources What Youll Bring 4-8 years experience in deploying and productionizing ML models at scale Strong knowledge in developing RAG-based pipelines using frameworks like LangChain & LlamaIndex Good understanding of various LLMs like Azure OpenAI and proficiency in their effective utilization Solid working knowledge of the engineering components essential in a Gen AI application, including Vector DB, caching layer, chunking, and embedding Experience in scaling GenAI or similar applications to accommodate a high number of users, large data size, and reduce response time. Expertise in Designing, configuring and using ML Engineering platform like Sagemaker, Azure ML, MLFlow, Kubeflow or other platforms Experience in Building ML pipelines, Troubleshooting ML models for high performance and scalability Experience with Spark or other distributed computing frameworks Strong programming expertise in Python, Scala or Java Experience in deployment to cloud services like AWS, Azure, GCP Strong fundamentals of machine learning and deep learning Up to date with recent developments in Machine learning and are familiar with current trends in the wider ML community Knowledgeable of core CS concepts such as common data structures and algorithms Excellent technical presentation skills (documentation, presentations, discussions) Good communicator with clear and concise, active listening and empathy skills. Collaborate well with teams with different backgrounds / expertise / functions Additional Skills: Understanding DevOps CI / CD, data security, experience in designing on cloud platform; Willingness to travel to other global offices as needed to work with the client or other internal project teams

JOB DESCRIPTION Designation: ML Operations Engineer Location: Hyderabad, India Work Mode: Office Reporting to: Principal Data Scientist Job Overview: At Foundation AI, As an ML Operations Engineer, you will design, develop, and maintain machine learning pipelines. You will work with structured and unstructured data. Your primary responsibility is to streamline the data science pipeline by automating the steps from Data gathering to Model deployment. Lifelong learning is crucial for long-term success, and we encourage you to stay current with the latest research by visiting conferences and sharing your knowledge throughout the enterprise. Responsibilities: You take responsibility for setting up maintainable and reliable ML pipelines on which our data scientists train the models. As part of an agile team, your ideas will be heard and impact the decision-making process. With our goal to invent for life, you will work on solutions that are both innovative and ethical. You will collaborate with ML engineers, data scientists, software developers, and DevOps engineers to have a real-world impact. Optimize the cost and latency of the services in production. Deploy the machine learning models in a scalable manner by utilizing the model-serving tools Skills and Tools: 3+ years of experience with Python, Linux skills, and machine learning principles 3+ years of experience in building and operating ML pipelines and data platforms in production 3+ years of experience in API design, distributed architectures, and orchestration of microservices Experience with container-based deployments (e.g. Docker, Kubernetes) Experience deploying deep learning models to a production environment Model lifecycle management (e.g. MLflow, KubeFlow) - 2+ years of experience in Workflow automation (e.g. Airflow) - 2+ years of experience in Version control of model files (like DVC) - 2+ years of experience in Exposure to Deep Learning frameworks, such as PyTorch, TensorFlow, etc - 2+ years of experience Experience working with the Cloud (AWS, Azure, GCP, etc.) - 2+ years of experience Personality and Working Practice: motivating attitude, profound communication, strong interpersonal skills, structured and analytical Experience in using Serving tools (RayServe, KServe) - 1 year(s) experience Experience in building and maintaining LLM pipelines - 1 year(s) experience Education: Bachelor's degree in Computer Science, Engineering, related field, or equivalent work experience. Our Commitment: At Foundation AI, we're committed to creating an inclusive and diverse workplace. We value equal opportunity and affirmative action principles, giving everyone an equal chance to succeed. We're dedicated to offering equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status. Upholding these values and adhering to applicable laws is paramount to us. For any feedback or inquiries, please contact us at careers@foundationai.com Learn more about us at www.foundationai.com