Job description

Job Summary: Core Generative AI & ML Expertise • Strong understanding of Generative AI models (LLMs, multimodal models, embeddings). • Hands-on experience with foundation models (e.g., GPT-style, Claude-style, LLaMA-style) and model adaptation techniques. • Expertise in prompt engineering , prompt orchestration , and agent-based frameworks . • Solid grounding in machine learning fundamentals , including supervised/unsupervised learning, evaluation metrics, and inference optimization. AI Architecture & System Design • Ability to design scalable, modular GenAI architectures for production use. • Experience with: • RAG (Retrieval-Augmented Generation) architectures • Vector databases (semantic search, embeddings indexing) • Multi-agent systems and workflow orchestration • Strong understanding of low-latency inference , model routing , and fallback strategies . • Knowledge of event-driven , microservices , and API-first architectures . Product Engineering & Integration • Experience integrating GenAI capabilities into customer-facing and internal products . • Ability to translate product requirements into AI-driven capabilities and technical designs. • Familiarity with A/B testing , feature flags, and iterative product releases involving AI. Data & Knowledge Engineering • Proficiency in data pipelines , feature engineering , and unstructured data processing . • Experience with: • Knowledge graphs • Metadata-driven architectures • Document ingestion and chunking strategies • Strong understanding of data quality, provenance, and governance for AI systems. Cloud, MLOps & Platform Skills • Strong experience in cloud-native environments (GCP). • Familiarity with MLOps practices , including: • Model versioning • Deployment pipelines • Monitoring, logging, and drift detection • Experience with containerization , Kubernetes , and CI/CD pipelines. • Knowledge of inference optimization and cost-control strategies. Security, Privacy & Responsible AI • Understanding of AI security risks (prompt injection, data leakage, model abuse). • Experience implementing guardrails , content filters , and policy enforcement . • Knowledge of responsible AI practices , including explainability, bias mitigation, and compliance. • Familiarity with data privacy regulations (e.g., GDPR, enterprise governance standards). Salary and Other Compensation : The salary for this position is between $160,000 - $200,000k depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans. Benefits : Cognizant offers the following benefits for this position, subject to applicable eligibility requirements: · 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 Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Cognizant will only consider applicants for this position who are legally authorized to work in the United States without requiring company sponsorship now or at any time in the future. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant. Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India.

Requirements

Must-haves

Core Generative AI & ML Expertise
Strong understanding of Generative AI models (LLMs, multimodal models, embeddings).
Hands-on experience with foundation models (e.g., GPT-style, Claude-style, LLaMA-style) and model adaptation techniques.
Expertise in prompt engineering , prompt orchestration , and agent-based frameworks .
Solid grounding in machine learning fundamentals , including supervised/unsupervised learning, evaluation metrics, and inference optimization.
AI Architecture & System Design
Ability to design scalable, modular GenAI architectures for production use.
Experience with: • RAG (Retrieval-Augmented Generation) architectures
Vector databases (semantic search, embeddings indexing)
Multi-agent systems and workflow orchestration
Strong understanding of low-latency inference , model routing , and fallback strategies .
Knowledge of event-driven , microservices , and API-first architectures .
Product Engineering & Integration
Experience integrating GenAI capabilities into customer-facing and internal products .
Ability to translate product requirements into AI-driven capabilities and technical designs.
Familiarity with A/B testing , feature flags, and iterative product releases involving AI.
Data & Knowledge Engineering
Proficiency in data pipelines , feature engineering , and unstructured data processing .
Experience with: • Knowledge graphs
Metadata-driven architectures
Document ingestion and chunking strategies
Strong understanding of data quality, provenance, and governance for AI systems.
Cloud, MLOps & Platform Skills
Strong experience in cloud-native environments (GCP).

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningapigcpkubernetesci/cd

Platform: cognizant
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave