Cognizant · 16 hours ago
Forward Deployed Context Engineering Lead, Associate Partner Consulting
New Jersey, United States
Full-time
Hybrid
Director/Executive
$137K/yr - $250K/yr
15+ years expCognizant is a leading technology company, and they are seeking a Forward Deployed Context Engineering Lead to design and govern AI solutions for financial services clients. This role involves collaborating with senior executives to create AI transformation roadmaps, while ensuring compliance with regulatory standards and overseeing the implementation of context architectures that enhance AI workflows.
ConsultingIndustrial AutomationInformation TechnologySoftwareSoftware Engineering
No H1B
Responsibilities
Partner with CFO, CIO, COO, Chief Risk Officer, and Chief Compliance Officer to define multi-year AI transformation roadmaps, with emphasis on use case prioritization, governance alignment, and risk-adjusted ROI
Work "forward deployed" at client sites, embedding within business units and technology teams to own end-to-end solution delivery from discovery through production and value realization
Lead discovery workshops to uncover high-value AI opportunities, frame problem statements, and shape outcome-driven implementations in core business processes (credit decisioning, market risk, AML/CFT, wealth advisory)
Own the complete context fabric that feeds LLMs and agents: data products, RAG pipelines, vector databases, knowledge graphs, semantic layers, tool schemas, memory stores, and orchestration patterns
Design and oversee implementation of enterprise-scale retrieval systems that integrate multiple data sources (core banking, risk repositories, regulatory data, market data, customer data warehouses) with sub-second latency and high recall
Architect tool landscapes for agents, defining function schemas, validation rules, pre/post-execution guardrails, and escalation patterns so agents safely interact with core systems (core banking APIs, trading platforms, CRM, regulatory reporting engines)
Establish context quality and freshness standards aligned to use case sensitivity: real-time for trading contexts, hourly for compliance contexts, daily for advisory contexts
Embed data lineage, quality controls, and metadata management into context pipelines to satisfy BCBS 239 principles (completeness, accuracy, timeliness, clarity, granularity) and emerging AI data governance expectations
Work with Chief Data Officer and data governance teams to ensure context data products meet regulatory lineage requirements, audit trails, and change management protocols
Design data product contracts that codify context completeness, freshness, and accuracy SLAs and make them machine-readable for automated quality gates
Define and operationalize comprehensive evaluation strategy covering accuracy, consistency, hallucination detection, bias, fairness, latency, cost, and regulatory compliance by use case
Establish baseline and continuous metrics for both offline benchmarking (held-out test sets, red teaming) and online monitoring (production feedback, human review, alert escalations)
Partner with Evaluation Engineering and Risk teams to implement automated quality gates in CI/CD pipelines, blocking unsafe or regressing models/prompts/context changes from deployment
Lead design and execution of red team exercises for high-risk use cases (credit decisioning, investment advice, transaction monitoring), including jailbreak detection, prompt injection, data leakage, and discriminatory output testing
Translate NIST AI RMF, ISO 42001, EU AI Act, and internal governance policies into architecture requirements: explainability, auditability, bias monitoring, human oversight, incident response
Collaborate with Chief Risk Officer, compliance, legal, and audit to define AI governance controls: model risk management, data governance, algorithm risk registers, impact assessments, and escalation workflows
Establish observability and monitoring frameworks that track AI system health (quality metrics, safety indicators, regulatory drift) and provide dashboards and alerts for business and risk stakeholders
Own technical platform strategy for AI solutions, including cloud selection (AWS, Azure, GCP), data platform selection (Snowflake, Databricks, Palantir Foundry, Microsoft Fabric), and integration architecture
Architect secure, scalable, multi-tenant AI infrastructure that meets financial services standards for security, auditability, disaster recovery, and regulatory reporting
Design and implement MLOps, LLMOps, and model governance workflows to ensure reproducibility, auditability, and rapid, safe iteration on AI solutions in production
Support go-to-market strategy for AI transformation engagements: solutioning, proposals, statement of work (SOW) development, and executive presentations to C-suite
Build thought leadership content (whitepapers, case studies, reference architectures) on context engineering, AI governance, and financial services AI transformation
Mentor and grow a practice or delivery team of context engineers, evaluation engineers, platform engineers, and solution architects to scale repeatable AI capability
Qualification
Required
15+ years of experience across software engineering, data engineering, data science, or AI/analytics, with at least 5–7 years leading AI/ML transformation initiatives
Proven track record leading large-scale AI or digital transformation programs at consulting firms (Deloitte, PwC, Accenture, Cognizant) or equivalent director/senior manager roles in financial services technology
Demonstrated expertise working in a forward-deployed or embedded model, owning end-to-end solution delivery from architecture through production launch and ongoing optimization
Hands-on technical expertise in modern AI stacks: LLMs, RAG, vector databases, cloud platforms, and ML engineering practices
Prior experience in financial services (investment banking, capital markets, wealth management, payments, insurance) or other regulated domains (healthcare, government)
Strong communication skills: ability to translate technical AI concepts for C-suite audiences and facilitate workshops with business and risk stakeholders
Preferred
Deep, hands-on understanding of LLM architecture, capabilities, limitations, and fine-tuning approaches; experience with GPT-4, Claude, LLaMA, and domain-specialized models
Advanced expertise in multi-agent orchestration patterns: hierarchical agents, collaborative agents, tool-using agents, memory strategies, and long-horizon planning for financial workflows
Proficiency with prompt engineering, in-context learning, chain-of-thought, and few-shot prompting for complex financial reasoning tasks (credit analysis, risk assessment, advisor-style interactions)
Production experience designing and implementing retrieval-augmented generation (RAG) systems, including chunking strategies, embedding models, vector databases (Pinecone, Weaviate, Milvus), and hybrid search (semantic + lexical)
Data engineering expertise: ETL/ELT pipelines, streaming architectures (Apache Kafka, Spark Structured Streaming), data quality frameworks, and metadata management catalogs
Knowledge graph and semantic layer experience: designing ontologies, entity resolution, relationship extraction, and knowledge graph querying for financial contexts (counterparties, instruments, risk factors, regulatory entities)
Experience with feature stores and data products as foundational context infrastructure; ability to define and operationalize data product contracts
Familiarity with AI safety and evaluation techniques: benchmark design, task-specific metrics, human-in-the-loop review, red teaming, jailbreak testing, and bias/fairness audits
Working knowledge of NIST AI Risk Management Framework, ISO 42001, BCBS 239 data governance principles, and emerging financial AI regulations (EU AI Act, Treasury AI guidance)
Experience with model risk management (MRM) frameworks, model cards, impact assessments, and governance workflows for high-risk AI systems in regulated environments
Production architecture and deployment experience on major clouds (AWS—Bedrock, SageMaker, EC2; Azure—Copilot, OpenAI Service, App Service; GCP—Vertex AI)
Proficiency with enterprise data platforms: Snowflake (architecture, Cortex AI), Databricks (LLMs, fine-tuning), Palantir Foundry (ontologies, apps), Microsoft Fabric (data engineering, AI services)
Security, compliance, and observability: encryption at rest/in transit, IAM, audit logging, HIPAA/SOX/GDPR compliance controls, and monitoring/alerting
Strong Python and at least one of TypeScript, Java, or Scala; experience building production ML/AI systems, not just prototypes
Hands-on MLOps, LLMOps, and CI/CD: infrastructure-as-code (Terraform, CloudFormation), containerization (Docker, Kubernetes), experimentation platforms, and model deployment pipelines
API design and integration: designing REST/GraphQL APIs, integrating AI systems into microservices architectures, event-driven systems, and enterprise applications (core banking, trading systems)
Benefits
Medical/Dental/Vision/Life Insurance
Paid holidays plus Paid Time Off.
401(k) plan and contributions.
Long-term/Short-term Disability.
Paid Parental Leave.
Employee Stock Purchase Plan
Company
Cognizant
Cognizant is a professional services company that helps clients alter their business, operating, and technology models for the digital era.
10001+ employees
Funding
Current Stage
Public CompanyTotal Funding
$0.24MKey Investors
Summit Financial Wealth Advisors
2025-03-08Post Ipo Equity
2016-11-18Post Ipo Equity· $0.24M
1998-06-19IPO
Leadership Team
Ravi Kumar S
Chief Executive Officer
Anil Cheriyan
CTO / EVP Strategy & Technology
Recent News
Indian Express
2026-02-04
Hindu Business Line
2026-02-03
Mexico Business
2026-02-03
Company data provided by crunchbase