Hirist.tech
Location
Bangalore | India
Job description
Unlock your career potential! Join our upcoming exclusive Tech Hiring Bootcamp in Bangalore Apply now to get pre-screened for coveted positions with top companies and many more! Don't miss this opportunity to fast-track your job application process. Apply todayRole : Lead - Machine Learning OpsAs a Lead - Machine Learning Ops in our data science department, youll have the chance to : - Lead a high performance MLOps team focusing on MLOps governance and making ML applications production ready- Work with ML Engineers & data engineers to optimize & scale the AI/ML POCs developed by data scientists wrt latency, throughput, cloud cost, maintainability etc.- Work with MLOps Engineers to design, deploy & monitor end-to-end ML applications- Mentoring, coaching, providing feedback, building career plans and assessing performance for your direct reports enabling a high performance MLOps team- Lead automation initiatives across components of ML lifecycle following agile development practices - data quality, data processing, model development, testing, model monitoring, ML platform adoption tracking & business value measurement- Be able to reason, influence stakeholders and drive ML product features from POC to production launch- Take end-to-end ownership of ML application pipelines in production, and be able toprocure cross-functional support as & when needed- Partner with data products & platforms team to influence their roadmap, so as to enable automated & improved ML governance practices and also evolve our data practices- Be able to hands-on develop high-performance, reliable, testable and maintainable code for ML applications when needed- Research and evaluate emerging technologies and techniques in the field of AI/ML, data engineering and data visualization to enhance the teams technical capabilities- Advocate for software and machine learning engineering best practices within data science teamWho are your stakeholders ?Internal :Engineers/Architects/Data Scientists : While MLOps Lead would sit within the Data Science team, he/she would be working very closely with rest of the tech function i.e. data engineers, software engineers, IT, DevOps & architects to deliver on their day to day tasksProduct Management : MLOps Lead responsibilities would require a lot of interactions with product management for clarifications around the requirements & business processes, to influence data products roadmap etc.External :Technology & Data Partners : This role would include a lot of interaction with our cloud & other technology providers to resolve tech issues, explore tech offerings & infrastructure needs, and may include exposure to data partners while working on ensuring data quality across the ML lifecycleWhat youll bring :- 5+ years of experience with designing and building robust, highly scalable and highly available data driven pipelines and applications- 3+ years of experience with ML lifecycle management including - model versioning, model and data lineage, model monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines- 3+ years of hands-on experience in building scalable distributed systems for model training, inferencing (batch & real time) & evaluation of machine learning models- Strong ability to design end to end ML systems and lead a technical roadmap, work with cross functional teams, with proven capacity to influence and build alignment- Deep understanding of machine learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection)- Experience with machine learning models integration with web applications- Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with automated governance- Software engineering fundamentals: version control systems, web services & APIs- Proficiency in Linux, Docker, Kubernetes, Jenkins, Version control systems- Good knowledge of machine learning & deep learning architectures, modelling frameworks & libraries (PyTorch, Tensorflow, Keras, scikit-learn etc.)- Experience with hardware resource management for ML training and/ordeployment (CPU/GPU/NPU)- Experience in using MLOps frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc. and building feature store- 5+ years of experience programming with Python or Spark or Java with strong understanding of data structures, algorithms & performance complexity- 5+ experience working with SQL databases, data warehouses & data lakes (MySQL/Hive/Redshift/Big Query etc.)- 3+ years experience working with cloud infrastructure (preferably AWS)- Experience with CI/CD systems, agile development processes, test driven development (unit tests & integration tests)- Excellent data analytical & debugging skills; problem solving, critical thinking & communication skills with strong attention to detailGood To Have :- Exposure to NoSQL/Document/Graph databases (such as MongoDB, DynamoDB, Cassandra, Neo4j)- Experience with ML platforms like SageMaker/Databricks/Azure ML/Vertex AI etc.- Familiarity with chat-based interfaces, conversational AI, and Generative AI- A professional ML Engineer certification (ref:hirist.tech)
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