4 Ml Architect Jobs

The Lead ML Architect will own the end-to-end design, development, and deployment of advanced ML and AI platforms, driving the architecture and technical direction for high-performance, scalable machine learning solutions. The role demands extensive hands-on expertise in building machine learning platforms, leveraging AWS SageMaker, Python-based frameworks, and parallel computing for efficient large-scale data processing. Key Responsibilities: Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms. Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies. Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models. Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems. Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms. Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization. Lead and mentor a high-performing team of ML engineers, promoting best practices, technical excellence, and continuous learning. Ensure compliance with model governance standards, data security best practices, and regulatory requirements (e.g., GDPR, HIPAA). Implement robust monitoring, alerting, and logging strategies to ensure consistent performance and rapid incident response for ML systems in production. Continuously optimize the performance, scalability, and cost-effectiveness of deployed ML models. Partner closely with product management and executive stakeholders to ensure alignment of ML platform development with strategic business objectives. Stay abreast of latest advancements in ML and AI technologies, proactively applying new techniques and tools to deliver superior outcomes. Day-to-Day Activities: Design, build, and maintain scalable ML platform infrastructure and tooling. Develop optimized machine learning models, leveraging advanced supervised and unsupervised techniques. Rapidly prototype and iterate on proof-of-concepts and transition successful prototypes into enterprise-grade solutions. Conduct comprehensive reviews and present findings clearly to technical and non-technical stakeholders. Continuously perform horizon scanning and research emerging ML trends, tools, and methodologies. Knowledge and Skills: Essential: Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker. Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn). Extensive hands-on experience with parallel computing frameworks and distributed processing. Proficient in SQL, ETL, data warehousing, and data modeling techniques. Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies. Proven capability of deploying machine learning models into production at scale. Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git). Preferred: Experience with Kubernetes orchestration systems. Familiarity with Snowflake data warehousing and NoSQL databases like MongoDB. Hands-on experience with messaging systems (e.g., Kafka). Exposure to distributed computing frameworks such as Hadoop, Spark, and Ray.

Job Title: Principal Machine Learning Architect Location: Bengaluru, Hybrid Department: Engineering / Data Science Reports To: Head of AI/ML About the Role: We are seeking a highly experienced Principal Machine Learning Architect to lead the design, development, and deployment of large-scale, high-impact ML solutions across our organization. As a thought leader, you will drive the ML roadmap, architect end-to-end solutions, and ensure our ML infrastructure is scalable, secure, and production-ready. Key Responsibilities: Architecture Leadership: Design and oversee the implementation of scalable, reliable, and efficient machine learning architectures. End-to-End Ownership: Lead the entire ML lifecycle from problem definition, data ingestion, feature engineering, model development, deployment, and monitoring. Strategic Guidance: Define best practices for ML system design, governance, LLM, NLP, Deep Learning, Machine Learning, RAG, MLOps, and responsible AI practices. Collaboration: Work cross-functionally with data scientists, ML engineers, product managers, and infrastructure teams to ensure alignment and integration. Innovation: Evaluate and incorporate new tools, techniques, and research to improve model performance and infrastructure. Mentorship: Provide technical mentorship to ML engineers and data scientists; promote a culture of technical excellence and continuous learning. Scalability: Architect and optimize ML solutions for scalability, performance, and maintainability in cloud and hybrid environments. Security & Compliance: Ensure models and data pipelines adhere to security, fairness, and compliance standards. Required Qualifications: 10+ years of experience in software engineering or data science with at least 5 years in ML architecture or applied ML roles. Advanced degree (MS/PhD) in Computer Science, Machine Learning, Data Science, or a related field. Proven experience building and deploying ML systems at scale (e.g., recommendation systems, NLP, computer vision, time series). Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Deep understanding of data infrastructure, distributed computing, and modern MLOps practices (e.g., MLflow, Kubeflow, Airflow). Familiarity with cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes). Strong knowledge of data privacy, model governance, and responsible AI principles. Preferred Qualifications: Experience with generative AI or LLM deployment. Contributions to open-source ML projects or research publications. Familiarity with data mesh, feature stores, or real-time ML pipelines. Strong communication and leadership skills.Role & responsibilities Preferred candidate profile

AI Ops Senior Architect 12 -17 Years Work Location - Pune/ Bengaluru/Hyderabad/Chennai/ Gurugram Tredence is Data science, engineering, and analytics consulting company that partners with some of the leading global Retail, CPG, Industrial and Telecom companies. We deliver business impact by enabling last mile adoption of insights by uniting our strengths in business analytics, data science and data engineering. Headquartered in the San Francisco Bay Area, we partner with clients in US, Canada, and Europe. Bangalore is our largest Centre of Excellence with skilled analytics and technology teams serving our growing base of Fortune 500 clients. JOB DESCRIPTION At Tredence, you will lead the evolution of Industrializing AI ” solutions for our clients by implementing ML/LLM/GenAI & Agent Ops best practices. You will lead the Architecture , Design & development of large scale ML/LLMOps platforms for our clients. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll be a trusted advisor to our clients in ML/GenAI/Agent Ops space & coach to the ML engineering practitioners to build effective solutions to Industrialize AI solutions THE IDEAL CANDIDATE WILL BE RESPONSIBLE FOR AI Ops Strategy, Innovation, Research and Technical Standards 1. Conduct research and experiment with emerging AI Ops technologies and trends. Create POV’s, POC’s & present Proof of Technology to use latest tools, Technologies & services from Hyper scalers focussed on ML, GenAI & Agent Ops 2. Define and propose new technical standards and best practices for the organization's AI Ops environment. 3. Lead the evaluation and adoption of innovative MLOps solutions to address critical business challenges. 4. Conduct meet ups, attend & present in Industry events, conferences, etc 5. Ideate & develop accelerators to strengthen service offerings of AI Ops practice Solution Design & Architectural Development 6. Lead Design & architecture of scalable model training & deployment pipelines for large-scale deployments 7. Architect & Design large scale ML & GenAI Ops platforms 8. Collaborate with Data science & GenAI practice to define and implement strategies of AI solutions for model explainability and interpretability 9. Mentor and guide senior architects in crafting cutting-edge AI Ops solutions 10. Lead architecture reviews and identify opportunities for significant optimizations and improvements. Documentation and Best Practices 11. Develop and maintain comprehensive documentation of AIOps architectures designs and best practices. 12. Lead the development and delivery of training materials and workshops on AIOps tools and techniques. 13. Actively participate in sharing knowledge and expertise with the MLOps team through internal presentations and code reviews. Qualifications and Skills: 1. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with minimum 12 years of experience 2. Proven experience in architecting & developing AIOps solutions – to streamline Machine Learning & GenAI development lifecycle 3. Proven experience as an AI Ops Architect – ML & GenAI in architecting & design of ML & GenAI platforms 4. Hands on experience in Model deployment strategies, Designing ML & GenAI model pipelines to scale in production, Model Observability techniques used to monitor performance of ML & LLM’s 5. Strong coding skills with experience in implementing best coding practices Technical Skills & Expertise Python, PySpark, PyTorch ,Java, Micro Services, API’s LLMOps – Vector DB, RAG, LLM Orchestration tools, LLM Observability, LLM Guardrails, Responsible AI MLOps - MLFlow, ML/DL libraries, Model & Data Drift Detection libraries & techniques Real Time & Batch Streaming Container Orchestration Platforms Cloud platforms – Azure/ AWS/ GCP, Data Platforms – Databricks/ Snowflake Nice to Have: Understanding of Agent Ops Exposure to Databricks platform You can expect to – Work with world’s biggest Retailers, CPG’s, HealthCare, Banking & Manufacturing customers and help them solve some of their most critical problems Create multi-million Dollar business opportunities by leveraging impact mindset, cutting edge solutions and industry best practices. Work in a diverse environment that keeps evolving Hone your entrepreneurial skills as you contribute to growth of the organization

Role: Tech lead/Architect Total exp 8-10 years Rel exp: 8 years Shift timings: 11 PM to 9 AM Bangalore, Hybrid Mode Description: Architect and implement scalable machine learning and deep learning models from scratch., Lead the development of information retrieval systems and relevance engineering solutions. Guide the team in implementing Retrieval-Augmented Generation (RAG) and vector-based search systems. Stay abreast of the latest advancements in AI, including LLMs, knowledge graphs, and generative AI. Spearhead the design, development, and deployment of cutting-edge AI solutions. You will lead a team of AI engineers, collaborate with cross-functional stakeholders, and drive innovation in machine learning, deep learning, and large language model applications. This role demands a strong foundation in AI technologies, hands-on experience in production-grade model deployment, and a strategic mindset to align AI initiatives with business goals. Hands-on experience with LangChain or similar frameworks,Skilled in building and tuning models using RAG, vector databases, and knowledge graphs