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.

AI/ML & Data Science Skills

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Proficient in Python with ML frameworks such as scikit-learn, TensorFlow, PyTorch, or equivalent.

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Solid understanding of supervised and unsupervised learning, time-series forecasting, classification, and clustering.

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Hands-on experience with AWS SageMaker for model training, hosting, monitoring, and MLOps pipelines.

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Familiarity with MLflow, Kubeflow, or equivalent MLOps tooling.

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Proficient in feature engineering, data wrangling, and working with structured and unstructured data at scale.

LLM, GenAI & NLP Skills

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

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Practical knowledge of RAG architecture, vector databases, and embedding pipelines.

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

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Experience building NLP pipelines including tokenization, NER, intent classification, summarization, and sentiment analysis.

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Familiarity with agentic frameworks such as LangChain, CrewAI, AutoGen, or equivalent.

Software Engineering & AWS Integration

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

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

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

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

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Proficient in SQL; experience with data lakes, streaming data, or event-driven architectures is an added advantage.

📌 Gen AI Developer (Bengaluru)
🏢 Mytech
📍 Bengaluru