24 days ago
Pune, IndiaSenior
Responsibilities
- Design and maintain scalable ML pipelines and platforms for ingestion, feature engineering, training, evaluation, inference, and deployment.
- Build and optimize large-scale data workflows using distributed systems for analytics and model training.
- Develop experiment-tracking, automated-reporting, and observability frameworks for monitoring model health, drift, anomalies, and performance.
- Optimize ML training workloads and inference using GPU-accelerated computing and Triton.
- Provide reusable components, SDKs, and APIs that enable teams to use AI insights and ML models.
- Automate ML jobs through CI/CD, workflow orchestration, and infrastructure-as-code practices.
- Partner with Product, Data Science, and Engineering teams to align ML infrastructure with business needs.
- Evaluate emerging Generative AI, MLOps, and Big Data technologies and bring research ideas into production.
- Build capabilities for RAG, reinforcement learning, embeddings, recommendation systems, personalization, forecasting, and anomaly detection.
Requirements
- At least four years of hands-on experience with petabyte-scale datasets and distributed systems.
- Bachelor’s degree in engineering, such as Computer Science or Information Technology, or an equivalent degree.
- Proficiency in SQL, Python, pandas, NumPy, and analytics or business intelligence tools.
- Experience or exposure to Neo4j, RAG approaches, embedding platforms, recommendation systems, personalization pipelines, or anomaly detection frameworks.
- Strong expertise with Spark, Hadoop, Kafka, Snowflake, and SparkSQL.
- Experience with Docker, Kubernetes, Airflow, MLflow, CI/CD, experiment tracking, and MLOps infrastructure.
- Programming proficiency in Python, Scala, or Java for data-intensive applications.
- Strong analytical, debugging, communication, and cross-functional collaboration skills.
- Passion for applied AI/ML and willingness to evaluate emerging technologies.
Benefits
- Hybrid work schedule with three days in the office and two days working remotely.
- Paternity and maternity leave.
- Healthcare insurance.
- Broadband reimbursement.
- Office kitchen with healthy snacks and drinks and catered lunches.
Tech Stack
Apache AirflowApache HadoopApache KafkaApache SparkDockerGrafanaKubernetesMLflowNeo4jNumPyPandasPythonSnowflakeSQL
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