23 days ago
Cleveland, OH, USASenior
Base Salary
$150k - $180k/yr
Responsibilities
- Design scalable, modular generative AI architectures for production use.
- Integrate GenAI capabilities into customer-facing and internal products.
- Translate product requirements into AI-driven capabilities and technical designs.
- Design RAG architectures, vector database solutions, multi-agent systems, and workflow orchestration.
- Support prompt engineering, prompt orchestration, agent-based frameworks, model routing, fallback strategies, and low-latency inference.
- Develop or support data pipelines, feature engineering, unstructured data processing, knowledge graphs, metadata-driven architectures, and document ingestion workflows.
- Apply MLOps practices including model versioning, deployment pipelines, monitoring, logging, and drift detection.
- Use containerization, Kubernetes, and CI/CD pipelines in cloud-native environments.
- Implement AI guardrails, content filters, policy enforcement, and responsible AI controls.
- Address data quality, provenance, governance, security, privacy, explainability, bias mitigation, and compliance.
- Support A/B testing, feature flags, iterative product releases, inference optimization, and cost-control strategies.
Requirements
- Strong understanding of generative AI models, including LLMs, multimodal models, and embeddings.
- Hands-on experience with foundation models such as GPT-style, Claude-style, and LLaMA-style models and model adaptation techniques.
- Expertise in prompt engineering, prompt orchestration, and agent-based frameworks.
- Strong machine learning fundamentals, including supervised and unsupervised learning, evaluation metrics, and inference optimization.
- Experience designing scalable GenAI architectures involving RAG, vector databases, semantic search, embeddings indexing, multi-agent systems, and workflow orchestration.
- Experience integrating GenAI into customer-facing and internal products.
- Proficiency with data pipelines, feature engineering, unstructured data processing, knowledge graphs, metadata-driven architectures, and document ingestion and chunking.
- Strong experience with cloud-native environments, particularly GCP.
- Familiarity with MLOps, model versioning, deployment pipelines, monitoring, logging, and drift detection.
- Experience with containerization, Kubernetes, and CI/CD pipelines.
- Understanding of AI security risks, guardrails, content filters, responsible AI, data privacy, and enterprise governance.
- Knowledge of GDPR, explainability, bias mitigation, compliance, data quality, provenance, and AI governance.
Benefits
- Hybrid work arrangement available from Cleveland, OH; Columbus, OH; Albany, NY; or Kansas City.
- Medical, dental, vision, and life insurance.
- Paid holidays and paid time off.
- 401(k) plan and contributions.
- Long-term and short-term disability coverage.
- Paid parental leave.
- Employee Stock Purchase Plan.
- Eligible for a discretionary annual incentive program based on performance and applicable plan terms.
Tech Stack
Categories
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.