Job Description
Roles &Responsibilities:
- Design, build, and maintain data pipelines and ETL/ELT workflows for structured and unstructured data sources.
- Develop, train, validate, and deploy machine learning and AI models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Collaborate with data engineers, business analysts, and product managers to translate analytical requirements into production-grade models and APIs.
- Optimize data processing performance and model inference pipelines for scalability and cost-efficiency (preferably on cloud platforms such as AWS, Azure, or GCP).
- Work with data visualization and BI teams to enable analytics dashboards that surface insights from AI models.
- Ensure data quality, governance, and compliance with organizational and regulatory standards.
- Communicate complex technical outcomes to non-technical stakeholders with clarity and impact.
Required Skills And Qualifications
- Bachelor s or Master s degree in Computer Science, Data Science, Statistics, or related field.
- 5+ years of hands-on experience in Data Engineering, Machine Learning, or AI solution development.
- Proficiency in Python, SQL, and data manipulation libraries such as Pandas, NumPy, or PySpark.
- Experience with data pipelines using Airflow, Databricks, AWS Glue, or Apache Spark.
- Strong understanding of ML algorithms (supervised, unsupervised, NLP, deep learning) and experience with at least one deployment-ready framework (TensorFlow /PyTorch).
- Experience deploying models through REST APIs, containers (Docker, Kubernetes), or serverless platforms (AWS Lambda, Azure Functions).
- Familiarity with cloud-based data platforms AWS (S3, Redshift, SageMaker), Azure (Synapse, ML Studio), or GCP (BigQuery, Vertex AI).
- Understanding of data governance, metadata management, and data security best practices.
- Strong communication and collaboration skills, capable of engaging with both business and technical teams.
- Proven ability to deliver in agile environments and manage competing priorities effectively.
Preferred / Good-to-Have Skills
- Experience with LLMs, Generative AI, or prompt engineering for text/image applications.
- Knowledge of data versioning (DVC) and feature stores for ML pipelines.
- Exposure to data visualization tools (Tableau, Power BI, Looker).
- Understanding of DevOps/MLOps CI/CD for model deployment.
- Certification in AWS AI/ML Specialty, Azure Data Scientist, or Google Cloud ML Engineer.
Key Attributes
- Strong analytical mindset with attention to detail.
- Curious and self-driven to explore emerging AI technologies.
- Effective communicator who can explain data and model insights to leadership.
- Team player with a focus on knowledge sharing and continuous learning.
Skills: data engineering,gcp,azure,machine learning,devops,pyspark,sql,mlops ci/cd,python,data visualization,deep learning,scikit-learn,generative ai,ml studio,nlp,pandas,etl/elt,tableau,metadata management,tensorflow,pytorch,prompt engineering,airflow,llms,databricks
