Title: AI-ML Engineer
What are my responsibilities
As an AI-ML Engineer, you are required to:
- Analyze structured and unstructured data sources to understand data lineage, identify relevant datasets, and support reliable data retrieval for downstream applications and analytical workflows.
- Work on data-driven solutions using Python, including data processing, transformation, analysis, and document/content generation workflows where required.
- Apply AI/ML techniques and modern models to improve data understanding, content extraction, enrichment, classification, and generation use cases.
- Collaborate with engineering and business teams to identify the right data sources, tables, columns, and transformation logic needed to enable scalable and accurate solutions.
- Contribute to the design and support of backend services and APIs for data-centric applications; familiarity with cloud-native and API-based architectures is an advantage.
- Support solutions that enable intelligent data identification and retrieval across diverse sources, including search-oriented approaches, semantic retrieval, and metadata-driven discovery patterns.
Qualification: Bachelor's or Master's in Computer Science & Engineering, Data Engineering, Data Science, Artificial Intelligence, Software Engineering, or equivalent.
Experience level: At least 3 - 5 years of hands-on experience in data engineering, data analysis, machine learning applications, with strong practical exposure to Python and SQL-based data retrieval.
Desired Knowledge & Experience:
- Strong hands-on experience in data transformation, data analysis, data quality, data profiling, and source-to-target mapping.
- Strong knowledge of enterprise data platforms and databases, including schema understanding, data lineage tracing, and structured/unstructured data analysis.
- Strong SQL skills and practical experience identifying the right tables, columns, joins, and transformation logic needed to retrieve and prepare business data.
- Hands-on expertise with Python is a must, including its use for data processing, analytics, automation, and integration of AI/ML models with data-driven workflows.
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- Knowledge of cloud technologies and frameworks in Microsoft Azure is preferred.
- Well-versed in relational database design and experience with processing and managing large data sets (multiple TB scale). (e.g. T-SQL, Microsoft SQL, Oracle)
- Good know-how & experience on Dataops (Data Orchestration / Workflow management & Monitoring Systems) and suggest improvements / inputs for CI/CD.
- Knowledge of Azure cloud-based data storage and services is preferred.
- Familiarity with FastAPI or similar API frameworks is preferred.
- Familiarity with semantic search, vector databases, or search/indexing technologies for efficient data identification and retrieval.
- Experience in working with LLMs, Agentic AI solutions
Experience in working with Knowledge Graph, Ontology Representation – desirable.
- Experience integrating data from multiple enterprise and external sources, and building applications or services that orchestrate retrieval, enrichment, and generation workflows, is a strong advantage.
- Exposure to modern interoperability patterns such as model context integrations, connector services, or server-based orchestration for AI/data applications is desirable.
- Strong written and verbal communication skills to collaborate effectively with global partners.
Required Soft skills & Other Capabilities:
- Great attention to detail and good analytical abilities.
- Good planning and organizational skills
- Collaborative approach to sharing ideas and finding solutions
- Ability to work independently and also in a global team environment.
