524 Mlops Jobs - Page 17

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Greetings from Blue Altair! Job Overview: We are seeking an experienced and highly skilled Data Science and AI Engineer to join our dynamic team. The ideal candidate will have 5+ years of experience working on cutting-edge data science and AI technologies across various cloud platforms with a strong focus to work on LLMs and SLMs. The role demands a professional capable of performing in a client-facing environment, as well as mentoring and guiding junior team members. Title: Consultant/Sr. Consultant - Data Science Engineer Experience : 5-8 years Location: Pune/Bangalore (Hybrid) Roles and responsibilities: Develop, implement, and optimize machine learning models and AI algorithms to solve complex business problems. Design, build, and fine-tune AI models, particularly focusing on LLMs and SLMs, using state-of-the-art techniques and architectures. Apply advanced techniques in prompt engineering, model fine-tuning, and optimization to tailor models for specific business needs. Deploy and manage machine learning models and pipelines on cloud platforms (AWS, GCP, Azure, etc.). Work closely with clients to understand their data and AI needs and provide tailored solutions. Collaborate with cross-functional teams to integrate AI solutions into broader software architectures. Mentor junior team members and provide guidance in implementing best practices in data science and AI development. Stay up-to-date with the latest trends and advancements in data science, AI, and cloud technologies. Prepare technical documentation and present insights to both technical and non-technical stakeholders. Requirement: 5+ years of experience in data science, machine learning, and AI technologies. Proven experience working with cloud platforms such as Google Cloud, Microsoft Azure, or AWS. Expertise in programming languages such as Python, R, Julia, and AI frameworks like TensorFlow, PyTorch, Scikit-learn, Hugging face Transformers. Knowledge of data visualization tools (e.g., Matplotlib, Seaborn, Tableau) Solid understanding of data engineering concepts including ETL, data pipelines, and databases (SQL, NoSQL). Experience with MLOps practices and deployment of models in production environments. Familiarity with NLP (Natural Language Processing) tasks and working with large-scale datasets. Hands-on experience with generative AI models like GPT, Gemini, Claude, Mistral etc. Client-facing experience with strong communication skills to manage and engage stakeholders. Strong problem-solving skills and analytical mindset. Ability to work independently and as part of a team and mentor and provide technical leadership to junior team members.

Role Responsibilities: Design and develop GenAI applications utilizing Python. Implement front-end solutions using Angular or React frameworks. Apply NLP techniques to enhance application functionalities. Optimize machine learning models for performance and scalability. Establish MLOps practices to streamline model deployment. Create and integrate APIs for seamless functionality between applications. Collaborate with cross-functional teams to define project requirements. Conduct code reviews to ensure adherence to best practices. Debug and troubleshoot applications to enhance user experience. Stay updated with industry trends and advancements in GenAI. Document project specifications and technological processes. Participate in regular team meetings to align on goals and challenges. Assist in training team members on GenAI technologies. Maintain version control for project codes and documentation. Provide technical support during product launches. Qualifications: Bachelor's or Master's degree in Computer Science or related field. Proven experience in GenAI development. Strong proficiency in Python programming language. Experience with front-end frameworks such as Angular and React. Hands-on expertise in Natural Language Processing (NLP). Familiarity with machine learning algorithms and models. Understanding of MLOps tools and practices. Experience in API development and integration. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Ability to work independently as well as in a team environment. Attention to detail with a focus on quality outcomes. Familiarity with database systems and management. Experience with version control systems like Git. Knowledge of agile methodologies is a plus.

Skills : Proficient: Languages/Framework: Fast API, Azure UI Search API (React) Cloud: Azure Cloud Basics (Azure DevOps) Gitlab: Gitlab Pipeline Ansible and REX: Rex Deployment Data Science: Prompt Engineering + Modern Testing Data pipeline development Understanding of AI/ML algorithms and their applications MLOps frameworks Knowledge of cloud platforms (Azure ML especially) Model deployment process Data pipeline monitoring Languages/Framework: Azure Open AI Data Science: Open AI GPT Family of models 4o/4/3, Embeddings + Vector Search Databases and ETL: Azure Storage Account, Postgresql, Cosmos Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) Knowledge of cloud platforms (AWS SageMaker, Google AI Platform) Expertise in data preprocessing, feature engineering, and model evaluation Understanding of software engineering principles (version control, CI/CD, containerization) Familiarity with distributed computing and big data tools (Spark, Hadoop) Ability to optimize models for performance and scalability Experience with Azure AI Search Desired skills* Azure DevOps; MLOps frameworks; Postgresql; Cosmos

Solutions Architect / Technical Lead - AI & Automation1 Key Responsibilities Solution Architecture & Development: Design end-to-end solutions using Node.JS (backend) and Vue.JS (frontend) for custom portals and administration interfaces. Integrate Azure AI services , Google OCR , and Azure OCR into client workflows. AI/ML Engineering: Develop and optimize vision-based AI models ( Layout Parsing/LP, Layout Inference/LI, Layout Transformation/LT ) using Python . Implement NLP pipelines for document extraction, classification, and data enrichment. Cloud & Database Management: Architect and optimize MongoDB databases hosted on Azure for scalability, security, and performance. Manage cloud infrastructure (Azure) for AI workloads, including containerization and serverless deployments. Technical Leadership: Lead cross-functional teams (AI engineers, DevOps, BAs) in solution delivery. Troubleshoot complex technical issues in OCR accuracy, AI model drift, or system integration. Client Enablement: Advise clients on technical best practices for scaling AI solutions. Document architectures, conduct knowledge transfers, and mentor junior engineers. Required Technical Expertise Frontend/Portal: Vue.JS (advanced components, state management), Node.JS (Express, REST/GraphQL APIs). AI/ML Stack: Python (PyTorch/TensorFlow), Azure AI (Cognitive Services, Computer Vision), NLP techniques (NER, summarization). Layout Engineering : LP/LI/LT for complex documents (invoices, contracts). OCR Technologies: Production experience with Google Vision OCR and Azure Form Recognizer . Database & Cloud: MongoDB (sharding, aggregation, indexing) hosted on Azure (Cosmos DB, Blob Storage, AKS). Infrastructure-as-Code (Terraform/Bicep), CI/CD pipelines (Azure DevOps). Experience: 10+ years in software development, including 5+ years specializing in AI/ML, OCR, or document automation . Proven track record deploying enterprise-scale solutions in cloud environments (Azure preferred). Preferred Qualifications Certifications: Azure Solutions Architect Expert , MongoDB Certified Developer , or Google Cloud AI/ML. Experience with alternative OCR tools (ABBYY, Tesseract) or AI platforms (GCP Vertex AI, AWS SageMaker). Knowledge of DocuSign CLM , Coupa , or SAP Ariba integrations. Familiarity with Kubernetes , Docker , and MLOps practices.

We are looking for an experienced AI ML Developers experience in data science specializing in machine learning python statistical modelling and big data technologies pyspark sql. The ideal candidate will have a strong background in developing and deploying machine learning models optimizing ML pipelines and handling largescale structured and unstructured data to drive business impact. Deep understanding of supervised and unsupervised learning including regression classification Multiclass classification clustering and NLP Proficiency in statistical analysis AB testing and causal inference techniques Experience with model deployment and MLOps in cloud environments AWS GCP Key Responsibilities Develop and deploy machine learning models and predictive analytics solutions for business impact Work with largescale structured and unstructured data to extract insights and build scalable models Design implement and optimize ML pipelines for realtime and batch processing Collaborate with engineering product and business stakeholders to translate business problems into data science solutions Apply statistical modeling AB testing and causal inference techniques to evaluate business performance Apply machine learning and statistical techniques for audience segmentation helping to identify patterns and optimise business strategies Drive research and innovation by staying updated with cuttingedge MLAI advancements and incorporating them into our solutions Optimize data science models for performance scalability and interpretability in production environments Mentor junior data scientists and contribute to best practices in data science and engineering

Machine Learning & Data Pipelines Strong understanding of Machine Learning principles, lifecycle, and deployment practices Experience in designing and building ML pipelines Knowledge of deploying ML models on AWS Lambda, EKS, or other relevant services Working knowledge of Apache Airflow for orchestration of data workflows Proficiency in Python for scripting, automation, and ML model development with Data Scientists Basic understanding of SQL for querying and data analysis Cloud and DevOps Experience Hands-on experience with AWS services, including but not limited to: AWS Glue, Lambda, S3, SQS, SNS Proficient in checking and interpreting CloudWatch logs and setting up alarm. Infrastructure as Code (IaC) experience using Terraform Experience with CI/CD pipelines, particularly using GitLab for code and infrastructure deployments Understanding of cloud cost optimization and budgeting, with the ability to assess cost implications of various AWS services

AI Ops/Monitoring Specialist openings at Advantum Health Pvt Ltd, Hyderabad. Overview: Were seeking an AI Ops/Monitoring Specialist to ensure the stability, transparency, and performance of AI systems in production. You will monitor, log, and troubleshoot AI and RPA models to ensure continuous reliability and compliance. Key Responsibilities: Monitor AI model health (drift, performance, latency, bias). Build dashboards and alerts using tools like Prometheus, Grafana, or Datadog. Establish SLAs and SLOs for AI/RPA models and pipelines. Collaborate with AI teams to integrate observability into model lifecycles. Document anomalies and assist in root cause analysis and mitigation. Qualifications: Bachelors in Data Science, IT, or a related field. 2+ years in systems monitoring, SRE, or MLOps. Experience with model monitoring tools (e.g., MLflow, Arize, WhyLabs). Familiarity with AI/ML lifecycles and performance metrics. Background in healthcare or compliance-heavy environments is ideal. Ph: 9177078628 Email id: jobs@advantumhealth.com Address: Advantum Health Private Limited, Cyber gateway, Block C, 4th floor Hitech City, Hyderabad. Do follow us on LinkedIn, Facebook, Instagram, YouTube and Threads Advantum Health LinkedIn Page: https://lnkd.in/gVcQAXK3 Advantum Health Facebook Page: https://lnkd.in/g7ARQ378 Advantum Health Instagram Page: https://lnkd.in/gtQnB_Gc Advantum Health India YouTube link: https://lnkd.in/g_AxPaPp Advantum Health Threads link: https://lnkd.in/gyq73iQ6

We are hiring Senior Machine Learning Engineer for an MNC Job Type : Direct , Fulltime role Location : PAN India (Remote) Senior Machine Learning Engineer Responsibilities Develop, implement, and maintain machine learning models using scikit-learn and SciPy. Build and deploy ML models for production use cases. Work with regression models and optimization techniques. Develop and integrate APIs using the Flask framework. Utilize GCP services (App Engine, Cloud Tasks, Dataflow, BigQuery, Bigtable, Vertex AI). Optimize CI/CD pipelines using Azure DevOps. Collaborate with cross-functional teams to deploy scalable ML solutions. Qualifications Strong proficiency in Python and core ML libraries Scikit-learn and SciPy. Hands-on experience with regression models and optimization techniques. Experience with Flask for API development and deployment. Proficiency in GCP services and ML operations on cloud infrastructure. Experience with CI/CD tools, especially Azure DevOps. Solid understanding of data structures, algorithms, and software engineering practices. Familiarity with Agile methodologies and technical documentation. Strong analytical, communication, and problem-solving skills.

JOB DESCRIPTION Senior MLE / Architect MLE (ML Ops) Chennai / Bangalore / Hyderabad (Hybrid) Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer fullstack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. About the role Curious about the role? What your typical day would look like? We are looking for a Senior / Lead ML Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will • Engage with clients to understand their business context • Lead a team of data scientists and engineers to embed AI and analytics into the business decision processes. • Perform a role that involves a combination of hands-on contribution and customer engagement. • Switch between roles of an Individual Contributor, team member and solution architect as demanded by each project What do we expect? 6+ years exp of which 3+ years of relevant data science experience and at least 3 years of software development experience. • Experience in devising creative analytical approaches to solve business problems • Developing and enhancing algorithms and models to solve business problem • Maintaining all models along with development and updating of code and process documentation • Designing a solution approach, leading a team to deploy and maintain it in production. • Proficient in Structured programming language is a must - such as Python, C/C++, Java is mandatory. Cloud concepts and significant hands-on experience with at least one cloud provider. Should be knowledgeable about the pros and cons of the various services and comfortable discussing the tradeoffs with stakeholders. • Strong SQL skills. • Good knowledge of Data science approaches, machine learning algorithms and statistical methods. • ETL and Data Engineering pipelines using Spark/PySpark. • Workflow orchestration tools like Airflow.

We are looking for a Senior Generative AI Engineer who is passionate about cutting-edge AI innovation and has significant hands-on experience in building and deploying Generative AI models. In this role, you will be responsible for designing, fine-tuning, and optimizing large language models (LLMs), implementing innovative GenAI solutions, and contributing to the architecture of AI-driven platforms that deliver real business value. You will collaborate with cross-functional teams including data scientists, machine learning engineers, product managers, and cloud infrastructure teams to build scalable, reliable, and secure AI systems. This is a high-impact position where you will directly influence the AI roadmap and innovation strategy. Key Responsibilities : - Design, develop, and fine-tune state-of-the-art Generative AI and LLM models tailored for various business use cases. - Build, integrate, and optimize solutions using transformer-based architectures (e.g., GPT, BERT, T5, LLaMA, Mistral). - Apply techniques such as fine-tuning, prompt engineering, RLHF (Reinforcement Learning from Human Feedback), and knowledge distillation to improve model performance. - Work with vector databases (e.g., FAISS, Pinecone, Weaviate) for implementing retrieval-augmented generation (RAG) pipelines. - Develop and deploy embedding models and integrate them into LLM pipelines. - Collaborate with engineering and product teams to deploy scalable AI systems using MLOps practices and CI/CD pipelines. - Leverage LangChain, Hugging Face Transformers, OpenAI APIs, and similar frameworks/tools to accelerate development. - Optimize model performance across different environments (cloud/on-premise). - Develop end-to-end pipelines, from data preprocessing to real-time inference and monitoring. - Ensure high standards of software quality, including testing, version control, code reviews, and documentation. - Stay up to date with the latest research in Generative AI and translate breakthroughs into production-ready solutions. Required Skills & Qualifications : - Experience : 7+ years in AI/ML, data science, or software engineering; at least 3 - 4 years in Generative AI/LLMs. - Advanced Python programming skills, including familiarity with object-oriented design and software engineering best practices. - Deep expertise in PyTorch, TensorFlow, Transformers (Hugging Face), LangChain, and OpenAI or Anthropic APIs. - Experience in LLM fine-tuning, parameter-efficient tuning methods (LoRA, PEFT), RLHF, and model evaluation. - Experience with embeddings, vector stores (FAISS, Pinecone), semantic search, and RAG systems. - Hands-on experience with AWS, GCP, or Azure; knowledge of MLOps tools (SageMaker, Vertex AI, MLflow, Kubeflow) for training, deploying, and monitoring models. - Familiarity with structured/unstructured data handling and integrating AI systems with SQL/NoSQL databases. - Strong analytical thinking, problem-solving ability, and a keen interest in research and innovation.

Job Summary: We are seeking a highly skilled and hands-on AI Engineer with 3+ years of proven experience in building and deploying solutions using Generative AI and Large Language Models (LLMs). You will work on cutting-edge applications leveraging transformer-based architectures, fine-tuning, prompt engineering, and scalable AI deployments. This role is ideal for engineers passionate about AI research and real-world productization of generative AI technologies. Key Responsibilities: Design, develop, and deploy solutions using LLMs (e.g., GPT, LLaMA, Mistral, Claude, PaLM, etc.) for various NLP and content generation tasks. Work on fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) pipelines. Integrate LLMs into enterprise applications with APIs and orchestrate workflows using Python, LangChain, or similar frameworks. Optimize model performance, latency, and cost for production use. Collaborate with data scientists, MLOps engineers, and product managers to deliver scalable AI features. Conduct experiments, analyze results, and publish internal findings or contribute to whitepapers. Ensure ethical, secure, and responsible use of AI technologies in all implementations. Required Skills & Experience: 3+ years of hands-on experience working with Generative AI, LLMs, and NLP technologies. Strong programming skills in Python and experience with libraries like Transformers (Hugging Face), LangChain, PyTorch, TensorFlow, etc. Proven track record of fine-tuning LLMs, developing embeddings, and working with vector databases (e.g., FAISS, Pinecone, Weaviate). Experience deploying models on cloud platforms (AWS, Azure, GCP) and using ML pipelines or MLOps tools. Solid understanding of deep learning, NLP architectures, tokenization, and evaluation metrics for generative models. Experience in API development and integration of LLMs into user-facing applications. Preferred Qualifications: Masters or PhD in Computer Science, AI/ML, Data Science, or related field. Experience with OpenAI APIs, Anthropic, Cohere, or open-source LLMs (e.g., Mistral, Falcon, LLaMA 3). Understanding of RLHF (Reinforcement Learning from Human Feedback) and model alignment techniques. Contributions to open-source AI projects or publications in GenAI/LLM.

Role Summary: This job involvesmanaging the day to day delivery of AI ML team, which will be responsible forbuilding, deploying and maintaining robust, scalable, and efficient ML models EssentialResponsibilities Lead and mentor team of data engineers, MLOps engineers, and machine learning engineers, to achieve project objectives & deliverables (development and deployment of ML models for AI use cases across the platform) Collaborate with business, engineering, infrastructure and data science teams to translate their needs or challenges into production-grade Artificial Intelligence and Machine Learning models for batch and real-time requirements Primary point of contact for US & Thryve leaders, ensuring clear communication and aligning with project goals and objectives Guide team of AI ML engineers with respect to business objectives and connecting the dots, helping to team to come up with optimal ML models based on use cases Manage day to day operation and ensure adherence to process, scope, quality and timelines Incumbent to assume role of an engagement coordinator between Thryve India team and US counterparts (techno functional) Highlight risks and concerns to leadership team, work with multiple stakeholders to establish alignment, contingency and or mitigation plans as required Experienced and proactive in tracking delivery statuses, project progress and take ownership of all deliverables from Thryve Effective communicator with team members, US stakeholders and management Stay up-to-date with the latest trends andadvancements in AI and ML and identify opportunities for the team to implementnew models and technologies Other duties as assigned or requested approximately 50% technical and 50% management Proposeand implement best engineering and research practices for scaling ML-poweredfeatures, with a goal to enable fast iteration of and efficient experimentationwith novel features Theexperience we are looking to add to our team Required Bachelor's degree in engineering or computerScience or related field 12 to 15 years of total experience with atleast 1+ year experience in leading / managing AI - ML projects achieving clear& measurable business objectives Good understanding of US Health insurance withat least 3+ years experience in the same Good experience / understanding of the entire MLOps from data ingestion to production Good understanding of AI-ML concepts &algorithms, including supervised, unsupervised, and reinforcement learning, as well as MLOps Ability to take successful, complex ideas fromexperimentation to production Knowledge / experience in Google cloud [GCP]or any public cloud platforms Excellent communication, presentation, andinterpersonal skills Proven experience in leading, motivating &inspiring teams to foster collaboration Proficient in identifying, assessing andcalling out risks and mitigation plans to stakeholders Experience in implementing processimprovements and learning from past projects to enhance future performance Able to analyze external and internalprocesses, and creating strategies for service delivery optimization Experience in working with diverse teams andstakeholders, understanding their needs and managing expectations

Senior Backend Developer Experience: 4 - 8 Years Exp Salary : Competitive Preferred Notice Period : Within 30 Days Shift : 10:00AM to 7:00PM IST Opportunity Type: Onsite (Ahmedabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Python, JS, MLOps, Microservices, SQL, DevOps, Generative AI Good to have skills : AWS, Azure Attri (One of Uplers' Clients) is Looking for: Senior Backend Developer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description Our engineering team is growing and we are looking to bring on board a Senior Backend Developer who can help us transition to the next phase of the company. You will be pivotal in refining our system architecture, ensuring the various tech stacks play well with each other, and smoothening the DevOps process. A must have well verse understanding of software paradigm, and curiosity to carve out designs of varying ML, MLOps, and LLMOps problem statements. You will determine to lead your team into right direction towards very end of implementation for underlined project. By joining our team, you will get exposure to working across a swath of modern technologies while building an enterprise-grade ML platform in the most promising area. Responsibilities: Be the bridge between engineering and product teams. Understand long-term product roadmap and architect a system design that will scale with our plans. Take ownership of converting product insights into detailed engineering requirements. Work break-down among team, and orchestrating the development of components for each sprint. Very well verse with solution designing, and documentation (HLD/LLD). Developing "Zero Defect Software" with extreme efficiency by utilizing modern cutting-edge tools (ChatGPT, Co-pilot etc). Adapt, and impart the mindset to build a unit of software that is secured, instrumented, and resilient. Author high-quality, highly-performance, and unit-tested code running on a distributed environment using containers. Continually evaluate and improve DevOps processes for a cloud-native codebase. Strong design skills in defining API Data Contracts / OOAD / Microservices / Data Models and Concurrency concepts. An ardent leader with an obsession for quality, refinement, innovation, and empowering leadership. Qualifications: Work Experience 5-7 years of experience with hands-on experience with development of full fledge Systems/Micro-services using Python, or JS programming. 3+ years experience having Senior engineering responsibilities. 3+ years of people mentorship/leadership experience managing engineers preferably with good exposure in leading multiple development teams. 3+ years of experience in object-oriented design, and agile development methodologies. Basic experience in developing/deploying cloud-native software using GCP / AWS / Azure. Proven track record building large-scale Product grade (high-throughput, low-latency, and scalable) systems. A well-versed understanding and designing skills of SQL/NoSQL/OLAP DBs. Up-to date with modern cutting-edge technologies to boost efficiency and delivery of team. (Bonus: To have an understanding of Generative AI frameworks/Libraries such RAG, Langchain, LLAMAindex etc.) Skills Strong documentation skills. As a team, we heavily rely on elaborate documentation for everything we are working on. Ability to take authoritative decision, and hold accountability. Ability to motivate, lead, and empower others. Strong independent contributor as well as a team player. Working knowledge of ML and familiarity with concepts of MLOps You will excel in this role if You have a product mindset. You understand, care about, and can relate to our customers. You take ownership, collaborate, and follow through to the very end. You love solving difficult problems, stand your ground, and get what you want from engineers. Resonate with our core values of innovation, curiosity, accountability, trust, fun, and social good. How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: Attri, an AI organization, leads the way in enterprise AI, offering advanced solutions and services driven by AI agents and powered by Foundation Models. Our comprehensive suite of AI-enabled tools drives business impact, enhances quality, mitigates risk, and also helps unlock growth opportunities. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

AI/ML Architect Location : Bangalore (Hybrid) Experience : 1015 years Key Requirements: Experience & Education 10+ years in total, 8+ years in AI/ML development 3+ years in AI/ML architecture Bachelor’s/Master’s in CS, AI/ML, Engineering, or similar Core Technical Skills Strong in TensorFlow, PyTorch Expertise in time series modeling and computer vision (object detection, facial/intrusion/anomaly detection) Hands-on with MLOps tools: MLflow, TFX, Kubeflow, SageMaker Experience in cloud (AWS, Azure, GCP) and edge AI (Jetson, Coral, OpenVINO) Proficient with Docker, Kubernetes, CI/CD pipelines Architecture & Integration Designed hybrid edge-cloud AI systems Integrated ML models into IIoT platforms Implemented model fusion with sensor, visual, and 3rd party data Leadership & Collaboration Mentored cross-functional teams (ML, Data, DevOps) Ensured security, compliance, and production readiness of AI models Translated AI strategy into business-aligned solutions

Key Responsibilities Responsible for building and maintaining robust machine learning pipelines ensuring efficient model deployment monitoring and lifecycle management within a cloud-based environment Extensive expertise in MLOps specifically with Google Cloud Platform GCP and Vertex AI and a deep understanding of model performance drift detection and GPU accelerators Build and maintain scalable MLOps pipelines in GCP Vertex AI for endtoend machine learning workflows Manage the full MLOps lifecycle from data preprocessing model training and deployment to model monitoring and drift detection Implement realtime model monitoring and drift detection to ensure optimal model performance over time Optimize model training and inference processes using GPU accelerators and CUDA Collaborate with cross functional teams to automate and streamline machine learning model deployment and monitoring Utilize Python 310 with libraries such as pandas NumPy and TensorFlow to handle data processing and model development Set up infrastructure for continuous training testing and deployment of machine learning models Ensure scalability security and high availability in all machine learning operations by implementing best practices in MLOps Requirements 5 years of experience in MLOps and building ML pipelines 3 years of experience in GCP Vertex AI Deep understanding of the MLOps lifecycle and automation of ML workflows Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow Strong experience in GPU accelerators and CUDA for model training and optimization Proven experience in model monitoring drift detection and maintaining model accuracy over time Strong problemsolving skills with the ability to work in a fast paced environment Knowledge of data versioning and model version control techniques Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks

Location: Any / Remote / Work From Home Employment Type: Full-time Job Summary: We are seeking skilled and innovative AI Engineers with a minimum 2-4 years of experience in developing and deploying AI solutions . The ideal candidate should have practical knowledge in AWS , Amazon Bedrock , LLMs , Claude AI , and Custom AI Development . You will contribute to the design and implementation of cutting-edge Generative AI (GenAI) and Mainstream AI applications across diverse business domains. This is an excellent opportunity to work with emerging technologies in an agile, fast-paced environment with full ownership of AI engineering pipelines. Key Responsibilities: Develop custom machine learning models and fine-tune foundation models for enterprise use Design and implement LLM-based and Generative AI solutions tailored to client use cases Work with OpenAI, Anthropic, HuggingFace models and their APIs to build secure, scalable AI systems Perform prompt engineering and experiment with parameter tuning and context optimization Build APIs and backend services to integrate AI models into product workflows Collaborate with product, data, and DevOps teams to deliver end-to-end AI-powered solutions Ensure best practices for AI model governance, security, performance, and ethical use Stay current with advancements in LLMs, vector databases (e.g., Pinecone, FAISS), and GenAI tooling Document architecture, experiments, and processes clearly and effectively Participate in peer reviews, client meetings, and cross-functional planning discussions Required Qualifications: Minimum 2-4 years of professional experience in AI/ML , with specific focus on LLMs and GenAI Strong experience working on AWS Bedrock , Lambda, and related services Proficiency in Python , including experience with libraries like HuggingFace, LangChain, etc. Familiarity with Anthropic , OpenAI , or other foundation models in production settings Experience with custom AI model development and model fine-tuning Good understanding of cloud-native AI deployments and API integrations Ability to apply prompt engineering techniques to optimize LLM responses Exposure to vector databases and RAG pipelines Strong problem-solving, communication, and team collaboration skills Preferred Qualifications (STRONG PLUS): Hands-on experience with LangChain, RAG pipelines, or agent frameworks Familiarity with AWS Sagemaker, DynamoDB, Lambda, or other AI tooling in AWS Knowledge of Docker or Kubernetes for containerizing AI workloads Understanding of MLOps principles and tools (MLflow, SageMaker Pipelines, etc.) Contributions to open-source AI projects or published GenAI applications AI/ML certifications Familiarity with enterprise use cases in biotech, healthcare, or manufacturing What We Offer: Remote work option Competitive salary package Exciting projects in the field of LLMs, AI, and cloud-native development Continuous learning opportunities in Generative AI and cloud AI tools A collaborative and fast-paced environment with a forward-thinking team Access to state-of-the-art tools and flexible project ownership How to Apply: Please submit your resume and cover letter outlining your relevant experience and why you'd be a great fit for Arocom. We differentiate candidates based on professionalism. We admire the new age methods like a video resume. If you have one please share the URL along with your resume. Your privacy is important to us, so we will not be contacting candidates by phone. If your application is selected, we will email you a link to schedule your interview at your convenient time. Please check your emails regularly, including your SPAM folder. Arocom encourages work from home and has a Bring Your Own Device (BYOD) policy. Employees / Consultants working from home are expected to use their personal laptops/desktops for work-related tasks.

What you will do We are seeking a highly skilled Machine Learning Engineer with a strong MLOps background to join our team. You will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines. Roles & Responsibilities: Collaborate with data scientists to develop, train, and evaluate machine learning models. Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring. Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment. Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency. Develop and implement monitoring systems to track model performance and identify issues. Conduct A/B testing and experimentation to optimize model performance. Work closely with data scientists, engineers, and product teams to deliver ML solutions. Guide and mentor junior engineers in the team Stay updated with the latest trends and advancements Basic Qualifications: Doctorate degree and 2 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Masters degree and 8 to 10 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Bachelors degree and 10 to 14 years of Computer Science, Statistics, and Data Science, Machine Learning experience OR Diploma and 14 to 18 years of years of Computer Science, Statistics, and Data Science, Machine Learning experience Preferred Qualifications: Must-Have Skills: Strong foundation in machine learning algorithms and techniques Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD) Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills Good-to-Have Skills: Experience with big data technologies (e.g., Spark), and performance tuning in query and data processing Experience with data engineering and pipeline development Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification Knowledge of NLP techniques for text analysis and sentiment analysis Experience in analyzing time-series data for forecasting and trend analysis Familiar with AWS, Azure, or Google Cloud; Familiar with Databricks platform for data analytics and MLOps Professional Certifications Cloud Computing and Databricks certificate preferred Soft Skills: Excellent analytical and fixing skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills.

Role & responsibilities 3+ years of experience in Spark, Databricks, Hadoop, Data and ML Engineering. 3+ Years on experience in designing architectures using AWS cloud services & Databricks. Architecture, design and build Big Data Platform (Data Lake / Data Warehouse / Lake house) using Databricks services and integrating with wider AWS cloud services. Knowledge & experience in infrastructure as code and CI/CD pipeline to build and deploy data platform tech stack and solution. Hands-on spark experience in supporting and developing Data Engineering (ETL/ELT) and Machine learning (ML) solutions using Python, Spark, Scala or R languages. Distributed system fundamentals and optimising Spark distributed computing. Experience in setting up batch and streams data pipeline using Databricks DLT, jobs and streams. Understand the concepts and principles of data modelling, Database, tables and can produce, maintain, and update relevant data models across multiple subject areas. Design, build and test medium to complex or large-scale data pipelines (ETL/ELT) based on feeds from multiple systems using a range of different storage technologies and/or access methods, implement data quality validation and to create repeatable and reusable pipelines Experience in designing metadata repositories, understanding range of metadata tools and technologies to implement metadata repositories and working with metadata. Understand the concepts of build automation, implementing automation pipelines to build, test and deploy changes to higher environments. Define and execute test cases, scripts and understand the role of testing and how it works. Preferred candidate profile Big Data technologies Databricks, Spark, Hadoop, EMR or Hortonworks. Solid hands-on experience in programming languages Python, Spark, SQL, Spark SQL, Spark Streaming, Hive and Presto Experience in different Databricks components and API like notebooks, jobs, DLT, interactive and jobs cluster, SQL warehouse, policies, secrets, dbfs, Hive Metastore, Glue Metastore, Unity Catalog and ML Flow. Knowledge and experience in AWS Lambda, VPC, S3, EC2, API Gateway, IAM users, roles & policies, Cognito, Application Load Balancer, Glue, Redshift, Spectrum, Athena and Kinesis. Experience in using source control tools like git, bit bucket or AWS code commit and automation tools like Jenkins, AWS Code build and Code deploy. Hands-on experience in terraform and Databricks API to automate infrastructure stack. Experience in implementing CI/CD pipeline and ML Ops pipeline using Git, Git actions or Jenkins. Experience in delivering project artifacts like design documents, test cases, traceability matrix and low-level design documents. Build references architectures, how-tos, and demo applications for customers. Ready to complete certifications

Description: Strong experience in ETL development, data modeling, and managing data in large-scale environments. - Proficient in AWS services including SageMaker, S3, Glue, Lambda, and CloudFormation/Terraform. - Hands-on expertise with MLOps best practices, including model versioning, monitoring, and CI/CD for ML pipelines. - Proficiency in Python and SQL; experience with Java is a plus for streaming jobs. - Deep understanding of cloud infrastructure automation using Terraform or similar IaC tools. - Excellent problem-solving skills with the ability to troubleshoot data processing and deployment issues. - Experience in fast-paced, agile development environments with frequent delivery cycles. - Strong communication and collaboration skills to work effectively across cross-functional team Role & responsibilities Preferred candidate profile