Job Description

Role Purpose

The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters

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Key Responsibilities:
- Design and deploy Gen AI solutions (e.g., chatbots, anomaly detectors) to automate incident triage, root cause analysis, and alerting in production environments.
- Integrate AI with existing tools like monitoring systems (e.g., Splunk, Dynatrace, Glassbox, CatchPoint), ticketing platforms (e.g., Remedy or ServiceNow), and databases (e.g., Oracle) to improve observability.
- Develop custom AI agents or scripts using LLMs (e.g., Claude, Llama, GPT, etc.,) for natural language querying of logs, metrics, and codebases, accelerating MTTR from hours to minutes.
- Collaborate with production support teams to identify toil-heavy processes, prototype AI fixes, and measure impact via KPIs like resolution time and ticket volume.
- Build and maintain AI pipelines for continuous learning from production incidents, ensuring models improve over time without human retraining.
- Ensure AI solutions comply with security, data privacy, and reliability standards in high-stakes financial or enterprise apps.
- Train and upskill support teams on AI tools, fostering a culture of AI-driven efficiency.

Preferred Skills:
- Proficiency in Gen AI frameworks & Transformers for building RAG (Retrieval-Augmented Generation) systems.
- Programming: Python (advanced), with experience in FastAPI, Streamlit for AI prototypes; SQL for database querying (Oracle preferred).
- Production support expertise: Incident management, log analysis, MTTR optimization in cloud (AWS/Azure) environment.
- AI ops: Model deployment via Docker/Kubernetes, monitoring with Prometheus/Grafana, handling drift and hallucinations.
- Data handling: Vector databases (Pinecone, FAISS), prompt engineering for domain-specific tasks like error prediction.

Desired Skills:
- MLOps tools: MLflow for CI/CD of AI models.
- Observability integration: Custom agents for tools.
- Domain knowledge: Global Banking, SRE practices (e.g., SLOs).
- Soft skills: Cross-functional communication, agile methodologies, experience converting AI in conservative teams.
- Emerging tech: Multimodal AI (e.g., vision for dashboard analysis), fine-tuning open-source models on proprietary incident data

Minimum Qualifications (Must‑Have) *

• 5–8 years total experience, including 3–5 years of hands‑on GenAI/automation work. 
• Strong Python skills for scripting, API integration, and automation. 

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