Generative AI Developer

Position Overview :

Trianz is hiring two AI Engineers for Project Concierto Agentic, a strategic initiative to embed intelligence and autonomous capabilities into the Concierto Agentic Project. These roles sit at the intersection of machine learning engineering, large language model integration, NLP, and AI-driven automation.

You will collaborate with senior Java developers, DevOps, QA, and Product Manager to design, build, and operationalize AI-powered features across cloud connectors, agentic workflows, and intelligent support tooling. Ideal candidates bring 4-7 years of experience spanning ML model development, LLM/GenAI application engineering, NLP pipelines, automation frameworks, and cloud-native AI integration on AWS.

Key Responsibilities :

AI/ML Model Development :

- Design and implement ML models for anomaly detection, predictive failure analysis, and connector health monitoring.

- Build and deploy supervised and unsupervised learning pipelines for IT operations analytics (AIOps) use cases.

- Develop time-series forecasting models to anticipate connector degradation and L4 incident spikes.

- Implement model versioning, A/B testing, automated retraining, and drift monitoring pipelines.

- Maintain feature stores, data quality standards, and model registries aligned with MLOps best practices.

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, and 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, and 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 :

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

- 4 to 7 years of hands-on experience spanning ML engineering, AI application development, or data science roles.

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

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

AI/ML & Data Science Skills :

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

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

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

- Familiarity with MLflow, Kubeflow, or equivalent MLOps tooling for model lifecycle management.

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

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 : tokenization, NER, intent classification, summarization, 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 micro services 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 server less 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, and LDAP).

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

- Exposure to responsible AI practices & bias detection, model explain ability (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.

- Fine-tuning, automation and real complex Agentic AI workflows. Need someone who built the product from scratch.

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.

Mandatory Skills :

- Gen AI Developer, Machine Learning, Python, Fast API.