Description

Key Responsibilities

LLM & Generative AI Integration

? Integrate large language model APIs (AWS Bedrock, OpenAI, Anthropic Claude) into Concierto connector orchestration workflows.

? Build Retrieval-Augmented Generation (RAG) pipelines for intelligent connector documentation search, incident summarization, and self-healing runbooks.

? Design and optimize prompt engineering strategies for operational use cases including root cause analysis, change advisory drafting, and test case generation.

? Develop AI agents using frameworks such as LangChain, CrewAI, or AutoGen for autonomous incident triage and connector lifecycle management.

? Deploy and manage LLM inference endpoints on AWS Lambda, ECS, or SageMaker with IAM-secured access controls.

NLP & Intelligent Log Analytics

? Develop NLP-based log parsing, event correlation, and semantic classification modules to accelerate L4 support triage.

? Build natural language query interfaces enabling operations teams to interrogate connector telemetry and CloudWatch logs in plain English.

? Apply Named Entity Recognition (NER), intent classification, and text summarization to convert raw incident data into actionable insights.

? Implement vector search and semantic similarity using AWS OpenSearch, Pinecone, or equivalent to power intelligent knowledge retrieval.

AI-Powered Automation Engineering

? Design agentic AI pipelines that autonomously diagnose, escalate, or resolve common Concierto connector issues without human intervention.

? Build AI-augmented test automation frameworks that generate, execute, and evaluate test cases for connector APIs using ML-based approaches.

? Develop self-healing test scripts leveraging pattern recognition and element-level ML locators.

? Integrate AI-based defect prediction and test coverage analysis into CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI).

? Automate connector deployment health checks, rollback triggers, and post-deployment validation using AI-driven observability.

AWS Cloud AI Integration

? Leverage AWS AI/ML services including SageMaker, Bedrock, Comprehend, Forecast, and OpenSearch for platform intelligence use cases.

? Integrate AI inference outputs with Java-based connector REST APIs through well-defined, versioned service contracts.

? Optimize ML model latency and throughput for real-time connector event classification and response at scale.

? Apply AWS security best practices ? IAM, KMS, VPC ? across all AI model data pipelines and inference endpoints.

? Design feature engineering pipelines from structured and semi-structured AWS event streams and connector logs.

Platform Observability & Intelligent Reporting

? Build AI-powered dashboards and alerting for Concierto connector KPIs using CloudWatch, Grafana, or equivalent.

? Generate AI-authored incident summaries, root cause analysis reports, and resolution recommendations.

? Develop executive-ready AI-generated performance narratives and weekly connector health digests.

Collaboration, Agile & Mentorship

? Work cross-functionally with Java developers, QA engineers, DevOps, and the PM to align AI modules with the Concierto Agentic roadmap.

? Participate in and contribute to agile ceremonies ? sprint planning, backlog grooming, retrospectives, and release planning.

? Maintain model cards, prompt libraries, API integration guides, and AI runbooks as living documentation assets.

? Mentor team members on AI/ML integration patterns, prompt engineering practices, and responsible AI principles.

Required Qualifications

Education & Experience

? Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Software Engineering, or equivalent.

? Minimum 2 years of demonstrated experience integrating AI/LLM/NLP capabilities in production environments.

? Prior experience delivering cloud-native AI solutions on AWS.

LLM, GenAI & NLP Skills

? Hands-on experience with LLM APIs: OpenAI, AWS Bedrock, Anthropic, or Hugging Face.

? Practical knowledge of RAG architecture, vector databases (OpenSearch, Pinecone, ChromaDB), and embedding pipelines.

? Proficient in prompt engineering ? zero-shot, few-shot, chain-of-thought, and tool-use patterns.

? Experience building NLP pipelines: tokenisation, NER, intent classification, summarisation, and sentiment analysis.

? Familiarity with agentic frameworks: LangChain, CrewAI, AutoGen, or equivalent.

Software Engineering & AWS Integration

? Proficient in Python and/or Java; experience exposing and consuming REST APIs in microservices architectures.

? Hands-on experience with AWS services: Lambda, S3, SQS, SNS, CloudWatch, ECS, and IAM.

? Familiarity with CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI.

? Working knowledge of Docker, Kubernetes, or serverless deployment patterns for AI workloads.

? Proficient in SQL; experience with data lakes, streaming data, or event-driven architectures is a plus.

Preferred Qualifications

? AWS Certified Machine Learning ? Specialty, AWS AI Practitioner, or AWS Certified Developer ? Associate.

? Experience with enterprise identity and access management platforms or IT connector ecosystems (IAM, OAuth 2.0, SCIM, LDAP).

? Background in AIOps, ITSM automation, cybersecurity analytics, or cloud infrastructure intelligence.

? Exposure to responsible AI practices ? bias detection, model explainability (SHAP/LIME), and AI governance frameworks.

? Experience with test automation frameworks enhanced by AI (Selenium, Playwright, Karate).

? Contributions to open-source AI/ML projects or published technical content on AI engineering topics.

Key Competencies

Technical	Professional
ML Model Development & MLOps	Analytical Thinking & Problem-Solving
LLM/GenAI API Integration	Cross-Functional Collaboration
NLP Pipeline Engineering	Clear Communication (Technical & Business)
Agentic AI & Workflow Automation	Agile & Sprint Delivery
AWS Cloud AI Services	Documentation & Knowledge Sharing
Python & REST API Engineering	Continuous Learning & Innovation Mindset