Gen AI Developer

Key Responsibilities:

AI/ML Model Development
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Design and implement ML models for anomaly detection, predictive failure analysis, and connector health monitoring.
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Build and deploy supervised and unsupervised learning pipelines for IT operations analytics and AIOps use cases.
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Develop time-series forecasting models to anticipate connector degradation and L4 incident spikes.
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Implement model versioning, A/B testing, automated retraining, and drift monitoring pipelines.
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Maintain feature stores, data quality standards, and model registries aligned with MLOps best practices.
LLM & Generative AI Integration
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Integrate LLM APIs such as AWS Bedrock, OpenAI, and Anthropic Claude into Concierto connector orchestration workflows.
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Build RAG pipelines for intelligent connector documentation search, incident summarization, and self-healing runbooks.
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Design and optimize prompt engineering strategies for root cause analysis, change advisory drafting, and test case generation.
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Develop AI agents using LangChain, CrewAI, AutoGen, or equivalent frameworks for autonomous incident triage and connector lifecycle management.
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Deploy and manage LLM inference endpoints on AWS Lambda, ECS, or SageMaker with IAM-secured access controls.
NLP & Intelligent Log Analytics
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Develop NLP-based log parsing, event correlation, and semantic classification modules to accelerate L4 support triage.
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Build natural language query interfaces for operations teams to interrogate connector telemetry and CloudWatch logs.
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Apply NER, intent classification, and text summarization to convert raw incident data into actionable insights.
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Implement vector search and semantic similarity using AWS OpenSearch, Pinecone, or equivalent solutions.

AI Engineer - Project Concierto Agentic
AI-Powered Automation Engineering
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Design agentic AI pipelines that autonomously diagnose, escalate, or resolve common Concierto connector issues.
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Build AI-augmented test automation frameworks that generate, execute, and evaluate test cases for connector APIs.
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Develop self-healing test scripts using pattern recognition and element-level ML locators.
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Integrate AI-based defect prediction and test coverage analysis into CI/CD pipelines.
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Automate connector deployment health checks, rollback triggers, and post-deployment validation using AI-driven observability.
AWS Cloud AI Integration
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Leverage AWS AI/ML services including SageMaker, Bedrock, Comprehend, Forecast, and OpenSearch.
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Integrate AI inference outputs with Java-based connector REST APIs through well-defined, versioned service contracts.
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Optimize ML model latency and throughput for real-time connector event classification and response at scale.
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Apply AWS security best practices including IAM, KMS, and VPC across AI data pipelines and inference endpoints.
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Design feature engineering pipelines from structured and semi-structured AWS event streams and connector logs.
Platform Observability & Intelligent Reporting
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Build AI-powered dashboards and alerts for Concierto connector KPIs using CloudWatch, Grafana, or equivalent tools.
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Generate AI-authored incident summaries, root cause analysis reports, and resolution recommendations.
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Develop executive-ready AI-generated performance narratives and weekly connector health digests.
Collaboration, Agile & Mentorship
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Work cross-functionally with Java, QA, DevOps, and Product Management teams to align AI modules with the Concierto Agentic roadmap.
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Participate in agile ceremonies including sprint planning, backlog grooming, retrospectives, and release planning.
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Maintain model cards, prompt libraries, API integration guides, and AI runbooks as living documentation assets.
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Mentor team members on AI/ML integration patterns, prompt engineering practices, and responsible AI principles.

Required Qualifications
Education & Experience:
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Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Software Engineering, or equivalent.
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4 to 7 years of hands-on experience in ML engineering, AI application development, or data science roles.
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Minimum 2 years of demonstrated experience integrating AI/LLM/NLP capabilities in production environments.
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Prior experience delivering cloud-native AI solutions on AWS.
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