About Vericence

Vericence is a digital engineering and technology consulting firm helping enterprises build AI-driven platforms, modernize legacy systems, and scale innovation through cloud, data, and intelligent engineering. We partner with global organizations to deliver high-impact technology solutions and build world-class engineering teams.

JOB SUMMARY:

This role will be part of the team responsible for developing and deploying Data Engineering Platform, AI, and Integration solutions. Primary responsibility will be to work closely with the Data Engineering team, Data Architects, AI/ML teams, Data Scientists, and business stakeholders to implement scalable data and AI solutions for the organization using Python, PySpark, SQL, and other big data and AI technologies. The role will be responsible for creating technical specification documents, data and AI solution designs, test plans, and supporting data and AI solutions across the enterprise.

The role will also contribute to the organization's AI transformation by identifying opportunities to leverage Generative AI, machine learning, LLMs, RAG, AI agents, and intelligent data engineering capabilities to improve data operations, analytics, automation, and business processes.

ROLES AND RESPONSIBILITIES:

- Understand business processes and how they are modeled in various systems.
- Work with business users, technology teams, data engineers, data scientists, AI teams, and executives to understand their data and AI needs and create creative solutions to fulfill them.
- Design and implement data structures, workflows, AI-enabled workflows, and integrations between enterprise platforms to ensure the accurate and timely execution of business processes.
- Maintain scalable data pipelines to support continuing increases in data volume, complexity, and AI/ML workloads.
- Adhere to established best practices on data integration/engineering, AI engineering, responsible AI, data management, and the future of our data infrastructure.
- Manage and improve the performance of databases, queries, data processing tools, AI/ML workloads, and solutions.
- Create and maintain data warehouses, data lakes, databases, tables, SQL queries, ingestion pipelines, predictive models, AI/ML pipelines, and downstream analysis.
- Write complex and efficient queries to transform raw data sources into easily accessible models for our teams and reporting platforms.
- Prepare and curate data for predictive, prescriptive, and AI/ML modeling, including feature engineering and model-ready datasets.
- Identify and analyze data patterns, trends, anomalies,



and relationships that can support business insights and AI use cases.
- Identify ways to improve data reliability, efficiency, quality, and AI readiness.
- Work with analytics, data science, AI, and wider engineering teams to automate data analysis and visualization needs, advise on transformation processes to populate data models, and explore ways to design and develop modern data and AI infrastructure.
- Identify and evaluate opportunities for Generative AI, LLMs, RAG, AI agents, and machine learning within data engineering and enterprise business processes.
- Collaborate with AI/ML teams to develop and operationalize AI/ML pipelines, model-serving workflows, feature pipelines, and AI-enabled data solutions.
- Support the development and implementation of RAG pipelines, vector search, embeddings, knowledge bases, and LLM-powered applications where appropriate.
- Evaluate AI solutions for scalability, accuracy, reliability, security, privacy, explainability, and production readiness.
- Establish appropriate data, model, and AI governance practices, including monitoring, lineage, quality, access control, and responsible AI considerations.
- Support the integration of AI and analytics outputs into enterprise applications, workflows, and operational processes.
- Collaborate, coordinate, and communicate across disciplines and departments.
- Ensure compliance with HIPAA regulations and requirements, including appropriate controls for AI/ML solutions processing PHI.
- Demonstrate Companys Core Competencies and values held within.
- Please note due to the exposure of PHI sensitive data this role is considered to be a High Risk and privileged Role.
- The position responsibilities outlined above are in no way to be construed as all encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.

Job Requirements (Education, Experience, and Training):

- Minimum Graduation Degree and 12+ years related experience, three (3) of which should be inclusive of experience with schema designing, data modeling, designing, building, and maintaining data processing systems. Bachelor’s degree in computer science, information technology, data engineering, AI/ML, or a similarly relevant field is highly preferred.




- Experience with advanced analytics tools for object-oriented/object-function scripting using languages such as Python, PySpark, Java, and others.
- Hands-on experience with AI/ML technologies, Generative AI, Large Language Models (LLMs), machine learning, natural language processing, or AI-enabled data platforms.
- Experience designing or implementing AI/ML data pipelines, model training/serving pipelines, feature engineering workflows, or MLOps/LLMOps practices.
- Experience with RAG architectures, vector databases/search, embeddings, prompt engineering, knowledge bases, or AI agent frameworks is highly preferred.
- Database development experience using ETL processes, SQL, Spark, or BigQuery and experience with Delta Lake and Data Warehouse, using Databricks, Snowflake, or similar products.
- Experience in triaging data issues, analyzing end-to-end data pipelines, and working with business users in resolving issues.
- Experience working with data governance/data quality and data security teams, specifically data stewards and security officers, in moving data pipelines and AI solutions into production with appropriate data quality, governance, security, privacy, and certification standards.
- Experience or exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics.
- Exposure to Generative AI and LLM application development, including RAG, vector search, embeddings, AI agents, model evaluation, and AI governance.
- Exposure to agile methodologies and capable of applying DevOps and increasingly DataOps/MLOps/LLMOps principles to data and AI pipelines.
- Ability to work with both IT and business teams in integrating analytics, data science, and AI outputs into business processes and workflows.
- Understanding of AI security, privacy, responsible AI, model governance, data protection, and regulatory considerations, particularly in environments containing sensitive or PHI data.
- An agile learner who brings strong problem-solving skills and enjoys working as part of a technical, cross-functional team to solve complex data and AI problems.
- Strong attention to detail when identifying data relationships, trends, anomalies, and AI/ML data quality issues.
- Ability to think through long-term impacts of key design decisions and handle failure scenarios across data and AI solutions.
- Ability to effectively share technical information and communicate technical issues and solutions to all levels of the business, including technical teams and executive stakeholders.

📌 Technical Product Delivery Manager - AI Experience (India)
🏢 Vericence
📍 India