Software Engineering - Group 2 - Lead I - Software Engineering

DescriptionClient Job Description: CDP MISSION: Our mission is to be the authoritative source of truth for customer data delivering timely, high-quality data at scale to power the contextual experiences that drive the growth of this company. Every customer profile must be accurate, trusted, and available when it matters, across every touchpoint, for the entire US adult population.

Job Overview

We are seeking a Senior AI/ML Engineer to lead the design and development of the advanced AI systems that make our Customer Data Platform (CDP) the authoritative source of truth for customer data covering the entire US adult population.

This role owns the intelligence layer of CDP: production-grade identity resolution at massive scale, and LLM-powered interfaces that make trusted customer data accessible to every stakeholder in the organization. You will architect systems that resolve billions of customer records into accurate, unified profiles and build the natural language interfaces that let business users query and understand that data without writing SQL.

You will drive architecture decisions, define best practices, and lead the development of systems where accuracy, trust, and timeliness are non-negotiable.

Job Responsibilities Identity Resolution

Design and lead end-to-end identity resolution architecture, combining probabilistic models, ML, and embedding-based techniques to build the authoritative customer identity graph

Build and optimize large-scale entity matching systems across billions of records and multiple data domains ensuring every US adult is accurately represented in CDP

Architect advanced candidate generation and blocking strategies (LSH, phonetic encoding, semantic similarity) that balance precision with computational feasibility at population scale

Design high-precision matching pipelines using ensemble approaches (rules + ML + LLM-based validation) to maximize accuracy of golden customer profiles

Develop scalable clustering and graph-based approaches for unified customer identity resolution with clear confidence scoring and auditability

Lead implementation of embedding pipelines and similarity search systems using transformer models for semantic-level identity matching

Job Responsibilities AI/LLM

Architect and build LLM-powered systems for entity resolution, including zero-shot and few-shot classification workflows that handle edge cases traditional models miss

Design and implement RAG-based architectures for enriching and contextualizing customer data from unstructured sources

Lead development of NLQ-to-SQL platforms, enabling business users to query CDP the authoritative source of truth using natural language

Translate ambiguous business questions into structured queries with schema awareness, semantic layers, and guardrails that protect data integrity

Define best practices for prompt engineering, evaluation, and LLM observability ensuring AI outputs meet the trust standards CDP demands

Design and optimize vector search architectures (Pinecone, Qdrant, pgvector) for large-scale retrieval across customer data

Evaluate and integrate emerging frameworks such as LangChain, LangGraph, and agentic workflows where they strengthen CDP capabilities

Education and Work Experience

Bachelor's or Master's degree in Computer Science, Data Science, or related field

6+ years of experience in ML/AI engineering

Proven experience building production-grade entity resolution or identity graph systems at scale

Experience designing LLM-based applications in enterprise environments with high accuracy and trust requirements

Technical Skills

Advanced programming: Python

Deep expertise in ML algorithms for similarity, classification, and clustering particularly in identity resolution contexts

Strong experience with transformer models, embeddings, and semantic search at population scale

Hands-on experience with LLM APIs and orchestration frameworks

Strong SQL and experience with distributed data processing (Spark, Dask)

Experience with vector databases and ANN search systems (FAISS, Pinecone, etc.)

Expertise in ML lifecycle management (MLflow or equivalent)

Understanding of data governance, privacy, and security requirements for customer identity data

Knowledge, Skills, and Abilities

Strong system design and architectural thinking for AI/ML systems at population scale

Ability to balance precision, recall, and scalability in identity resolution systems understanding that accuracy directly impacts CDP's authority as the source of truth

Strong understanding of data semantics and customer domain modeling across diverse data sources

Leadership in driving AI engineering best practices, standards, and quality benchmarks

Ability to collaborate across data engineering, product, security, and business teams to deliver trusted customer intelligence

Licenses and Certifications

At least 18 years of age

Legally authorized to work in the United States

Travel

Travel Required: No

Job Description: Expectations from this role:

Act creatively to develop applications and select appropriate technical options, optimizing application development, maintenance and performance by employing design patterns and reusing proven solutions, account for others' developmental activities

1. Interpret the application/feature/component design to develop the same in accordance with specifications.

2. Code, debug, test, document and communicate product/component/feature development stages.

3. Validate results with user representatives; integrates and commissions the overall solution

4. Select appropriate technical options for development such as reusing, improving or reconfiguration of existing components or creating own solutions

5. Optimises efficiency, cost and quality.

6. Influence and improve customer satisfaction

7. Set FAST goals for self/teamTypical performance measures:

1. Adherence to engineering process and standards (coding standards)

2. Adherence to project schedule / timelines

3. Number of technical issues uncovered during the execution of the project

4. Number of defects in the code

5. Number of defects post delivery

6. Number of non compliance issues

7. On time completion of mandatory compliance trainingsPerformance Areas:

Code as per design

Follow coding standards, templates and checklists

Review code - for team and peers

Documentation:

Create/review templates, checklists, guidelines, standards for design/process/development

Create/review deliverable documents. Design documentation, r and requirements, test cases/results

Configure:

Define and govern configuration management plan

Ensure compliance from the team

Test:

Review and create unit test cases, scenarios and execution

Review test plan created by testing team

Provide clarifications to the testing team

Domain relevance:

Advise Software Developers on design and development of features and components with a deep understanding of the business problem being addressed for the client.

Learn more about the customer domain identifying opportunities to provide valuable addition to customers

Complete relevant domain certifications

Manage Project:

Manage delivery of modules and/or manage user stories

Manage Defects:

Perform defect RCA and mitigation

Identify defect trends and take proactive measures to improve quality

Estimate:

Create and provide input for effort estimation for projects

Manage knowledge:

Consume and contribute to project related documents, share point, libraries and client universities

Review the reusable documents created by the team

Release:

Execute and monitor release process

Design:

Contribute to creation of design (HLD, LLD, SAD)/architecture for Applications/Features/Business Components/Data Models

Interface with Customer:

Clarify requirements and provide guidance to development team

Present design options to customers

Conduct product demos

Manage Team:

Set FAST goals and provide feedback

Understand aspirations of team members and provide guidance, opportunities, etc

Ensure team is engaged in project

Certifications:

Take relevant domain/technology certification

Skill Examples:

1. Explain and communicate the design / development to the customer

2. Perform and evaluate test results against product specifications

3. Break down complex problems into logical components

4. Develop user interfaces, business software components

5. Use data models

6. Estimate time and effort required for developing / debugging features / components

7. Perform and evaluate test in the customer or target environment

8. Make quick decisions on technical/project related challenges

9. Manage a Team,

10. Maintain high motivation levels and positive dynamics in the team.

11. Interface with other teams, designers, and other parallel practices

12. Set goals for self and team. Provide feedback to team members

13. Create and articulate impactful technical presentations

14. Follow high level of business etiquette in emails and other business communication

15. Drive conference calls with customers addressing customer questions

16. Proactively ask for and offer help

17. Ability to work under pressure, determine dependencies, risks, facilitate planning; handling multiple tasks.

18. Build confidence with customers by meeting the deliverables on time with quality.

19. Estimate time and effort, resources required for developing / debugging features / components

20. Make on appropriate utilization of Software / Hardware s.

21. Strong analytical and problem-solving abilitiesKnowledge Examples:

1. Appropriate software programs / modules

2. Functional and technical designing

3. Programming languages - proficient in multiple skill clusters

4. DBMS

5. Operating Systems and software platforms

6. Software Development Life Cycle

7. Agile - Scrum or Kanban Methods

8. Integrated development environment (IDE)

9. Rapid application development (RAD)

10. Modelling technology and languages

11. Interface definition languages (IDL)

12. Knowledge of customer domain and deep understanding of sub domain where problem is solved

Additional Sills: agentic ai,api integration,generative ai,machine learning

Skills:

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