Data Science Engineer

Job Description

Job Description :

We take pride in training and deploying solutions that not only utilize state-of-the-art ML, DL, GenAI techniques but also contribute to the research community with multiple peer-reviewed publications. The success of these systems depends heavily on massive events and dimension datasets, real time and batch data pipeline infrastructure and fault tolerant API systems which adapt to all seasons including very high scale season sales.

Roles and Responsibilities :

– Design and build resilient, scalable, online metric measurable ML DL recommender APIs.

– Build cost-effective yet scalable, ultra low latency semi-real-time event streams.

– Build deep learning model pipelines and pipeline abstractions, python libraries partner with applied data scientists to build world class agile, incremental, sequential learners.

– Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration

– Experiment and upgrade data science (DS) inference systems, build best in class restful frameworks.

– Streamline OS and python environment migrations, python version migrations co-own end to end production model service testing, capacity planning, testing, container/service health monitoring and alert automation.

– Co-create DS service asynchronous logging modules, rate limiters & circuit breaker modules, automations for smart service management in kubernetes clusters, and integrate with code/PII data protection modules.

– Build generic and charter centric derived event tables stitching events, features, and user feedback loop with appropriate partitions to stream line sequential or online ML learners, contextual bandit learners etc.

– GPU ops opportunity : Learn and optimise GPU services for encoder/predictor models using Tensor RT, Triton and other frameworks and be a champion of GPU ML inference optimisations across org.

– Opportunity to learn, own and add capabilities to offline ML DL model performance evaluation metric systems (MLflow, Arize, W&B, Neptune etc).

– Build ML/DL observability 2.0 modules with a combination of statistical, causal ML, and LLMs-as-judge.

– Co-own cost optimisation to maximise ratio of impact per user vs infra cost per user.

– Assist data science to improve dev cost efficiencies.

– Opportunity to co-create LLM as a judge framework

– Opportunity to streamline embedding ANNs, HNSW, quantisations and vector databases.

– Opportunity to build services with open sourced SLMs and computer vision models.

– Opportunity to experiment and deploy Agentic AI solutions with pre-trained LLMs and context/prompt engineering principles and best in class RAG/graphRAG/API retrieval mechanisms.

– Build a culture for enforcing strong production coding discipline in APIs, and design and solution documentation.

– Work with data analysts, data experts, product/engg and create custom queries and pipelines such that applied DS can efficiently create A/B metric dashboards and conduct error analyses and faster A/B iterations.

– Strong engineering mindset – build automated monitoring, alerting, self healing capabilities.

– Review the design and implementation in collaboration withArchitects and advocate latest DL/ML engineering practices amongst tech.

– Collaboration : Work closely with the Product Managers, Platforms and Engineering teams to ensure smooth deployment and integration of ML models into Myntra production systems.

Desired skills and experience :

– 3 to 4.5 Years hands on experience and proven expertise with building end to end, complex & robust complex and large Data Engineering pipelines on PySpark or scala.

– 1+ years experience especially in both ML batch feature pipelines, and also ideally/optionally near-real-time kafka consumer pipelines (Spark Structured streaming or Flink).

– Breadth experience working with data blobs, delta lakes, storage for ML workflows.

– 3+ years experience in ML API and ML pipeline engineering is must

– 2+ years excellent coding experience in Python, pyspark(Python3), Flask/Falcon/FastAPI.

– Solid experience in Kafka consumer/producers, read/write connectors to aerospike/redis DBs.

– Experience with ML orchestration tools (Airflow, Kubeflow, MLFlow)

– Must have experience with Qdrant/MIlvus or other vector DBs.

– Understanding of Architecture and Design of Data Engineering products. Be able to articulate the trade offs

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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