Machine Learning Engineer
2 days ago
Job Title: Machine Learning Engineer
Overview
The Machine Learning Engineer is a hybrid applied scientist and engineer role within the AI R&D team. This position combines deep technical expertise in machine learning, data engineering, and MLOps with strong business engagement skills. The role involves identifying ML-driven opportunities that deliver measurable business value, building robust ML pipelines, conducting applied research, and collaborating closely with cross-functional teams such as Product, Operations, R&D, Finance, and Services.
Key Responsibilities
- Build and maintain end-to-end ML pipelines, including data ingestion, preprocessing, training, deployment, and monitoring.
- Support fine-tuning and optimization of LLMs and other models for organization-specific use cases.
- Collaborate with IT and Digital Solutions to ensure scalable, secure, and compliant ML infrastructure.
- Lead exploratory workshops across business functions to identify high-value ML opportunities.
- Translate business challenges into structured ML problem statements with ROI and feasibility analysis.
- Work with solution architects to integrate ML models into business workflows and systems.
- Act as an advocate and thought leader for ML adoption, promoting best practices and organizational literacy.
Key Performance Indicators (KPIs)
- Reliable and scalable ML pipelines deployed and maintained.
- Timely delivery of ML contributions for pilot and production AI projects.
- Completion of 23 high-impact ML projects per year delivering measurable business value.
- Positive feedback from business stakeholders and leadership.
- Compliance with IT/DS guidelines for scalability, governance, and security.
Skills & Qualifications
Education
- Master's degree in Computer Science, Data Engineering, or related field.
Technical Skills
- Hands-on experience with: TensorFlow, PyTorch, Hugging Face, scikit-learn, XGBoost.
- MLOps proficiency: MLflow / W&B, Kubeflow, Airflow / Prefect, Docker/Kubernetes, cloud platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
Strong data engineering expertise:
SQL, Spark/Databricks
- Data warehouses (Snowflake, BigQuery, Redshift)
Vector DBs (Pinecone, Weaviate, Milvus, FAISS)
Experience building APIs for ML model integration (FastAPI, Flask, REST, GraphQL).
Business & Soft Skills
- Proven capability to lead exploratory workshops with business teams.
- Excellent communication, facilitation, and technical storytelling skills.
- Ability to translate business needs into actionable ML solutions.
Reporting Line
- Reports to: AI R&D Leader
- Works closely with solution architects, automation engineers, external consultants, and business stakeholders.
Additional Submission Note (Important)
Kindly share the below information while submitting profiles:
- Minimum 9+ years of experience with a strong background in Machine Learning.
- Number of end-to-end ML model implementations / deployments completed in production.
- Experience working with global, multi-cultural stakeholders, including distributed teams (US, Europe, APAC).
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