Specialist II - Data Science

Specialist II - Data Science

This role will closely partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive evolution toward agentic AI systems.

Key responsibilities include:

• GenAI Enablement & Integration
• Build and operationalize LLM-powered applications using:
• Retrieval-Augmented Generation (RAG)
• Embeddings pipelines
• Prompt orchestration and evaluation frameworks
• Design and implement vector search systems using Amazon OpenSearch
• Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability
• Integrate supporting infrastructure:
• Amazon ElastiCache (Redis) for session state and caching.
• DynamoDB for scalable, low-latency data access

Implement agentic workflows using frameworks such as:

• LangGraph, AutoGen, CrewAI (or equivalent)

Integrate with LLM frameworks like:

• LangChain, LlamaIndex (tool calling, retrieval orchestration, context management)

Define standards for:

• Tool integration
• Context-sharing patterns (MCP-style designs)
• Evaluate LLM models and retrieval strategies across:
• Latency
• Cost
• Accuracy
• Context limitations
• Data Pipelines & Knowledge Engineering
• Design and build scalable data pipelines using Databricks and Apache Spark
• Implement:
• Data ingestion and transformation pipelines
• Document processing (chunking, metadata tagging)
• Embedding generation and indexing
• Ensure high data quality standards:
• Validation, completeness, consistency, monitoring
• Implement data governance frameworks:
• Data classification and access controls
• Retention policies
• Auditability and lineage tracking.
• Backend Services & APIs:
• Develop backend services exposing AI capabilities through secure and scalable APIs
• Define best practices for:
• API contracts and versioning
• Reliability (retry logic, circuit breakers, idempotency)
• Enable reusability of platform capabilities across teams and applications
• Deployment, MLOps & Operational Excellence
• Build and manage CI/CD pipelines for AI and data workloads
• Deploy production systems using:
• Docker (containerization)
• Kubernetes (orchestration)
• Implement deployment strategies:
• Blue/green deployments
• Canary releases
• Rollback strategies
• Feature flags
• Ensure system reliability through:
• Monitoring (latency, failures, cost, data freshness)
• Alerting and observability
• Secrets management and least-privilege access
• Optimize platform performance and cost
• LLM Observability, Evaluation & Quality
• Define and track GenAI quality metrics:
• Grounding / faithfulness
• Retrieval relevance
• Response consistency
• Latency and cost per request
• Implement:
• Prompt/version tracking
• Offline evaluation pipelines
• Continuous improvement workflows
• LLM Security, Safety & Compliance
• Implement secure AI systems with:
• Access control and authentication
• Data protection policies
• Responsible AI guardrails
• Ensure compliance with best practices in:
• AI safety
• Data privacy
• Monitoring and auditability

Required skills include:

• Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)
• Hands-on experience with AWS ecosystem
• Expertise in:
• OpenSearch (vector search)
• Neptune (graph databases)
• DynamoDB and Redis (ElastiCache)
• Experience with:
• LangChain / llamaIndex
• Agentic AI frameworks (LangGraph, AutoGen, CrewAI)
• Strong programming skills (Python preferred)
• Experience with Databricks and Apache Spark
• Solid understanding of:
• Data pipelines
• Distributed systems
• API design
• Preferred skills experience with:
• Model evaluation frameworks and LLM observability tools
• AI governance and compliance frameworks
• Kubernetes and advanced MLOps practices
• Familiarity with:
• Model Context Protocol (MCP) patterns
• Agent-based architectures

Qualifications include:

• Bachelor's or Master's degree in:
• Computer Science / Data Science / AI / related field
• Proven experience building production-grade AI platforms and systems
• Strong background in end-to-end AI/ML lifecycle delivery

Soft skills include:

• Strong problem-solving and analytical thinking
• Ability to communicate complex AI concepts clearly
• Collaborative and cross-functional mindset
• Ownership-driven and proactive execution

TekWissen® Group is an equal opportunity employer supporting workforce diversity.