FORWARD DEPLOYEMENT ENGINEER

Must Have Technical/Functional Skills
10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and Production-grade Software Engineering.
Strong hands-on expertise in Python, PySpark, SQL, REST APIs, FastAPI, and enterprise application integration.
Experience designing and building AI-powered applications using Claude, Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases, Semantic Search, and Agentic AI patterns.
Deep understanding of Data Engineering frameworks, including ETL/ELT, batch and streaming pipelines, data quality, metadata management, lineage, observability, and DataOps practices.
Experience implementing and operationalizing MLOps / LLMOps capabilities including model lifecycle management, CI/CD pipelines, MLflow, monitoring, deployment automation, rollback, and production support.
Strong knowledge of cloud and modern data platforms, including compute, storage, integration, orchestration, security, networking, monitoring, and cost optimization services.
Hands-on experience with Git, Docker, Kubernetes, Airflow, MLflow, CI/CD, and distributed data processing frameworks.
Strong understanding of enterprise security and governance principles, including IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.
Ability to translate business requirements into scalable solution designs, define non-functional requirements, estimate effort, and drive production-ready implementation.
Experience working with Banking, Financial Services, or Insurance (BFSI) data environments and familiarity with regulatory, security, privacy, and resilience requirements.
Strong client-facing consulting skills with experience conducting discovery workshops, solution shaping, rapid prototyping, stakeholder communication, and technical presentations.
Bachelor s degree in computer science, Engineering, Information Systems, Data Management, or a related discipline.
Preferred certifications in Cloud Platforms, Kubernetes, AI Engineering, MLOps, Data Engineering, Enterprise Architecture, Security, or Program Delivery.
Experience working in a forward-deployed delivery model by embedding with client SMEs, business users, and engineering teams through solution validation, production deployment, adoption, and handover.
Strong hands-on data discovery skills, including profiling unfamiliar client data with Python, pandas, PySpark, and SQL; assessing coverage and quality; and quantifying volumes, distributions, edge cases, and business-process gaps before development.
Ability to rapidly build thin working solutions using real client data and APIs, validate them with users, and iteratively harden them with automated tests, security controls, error handling, observability, and production support procedures.
Experience creating business-relevant evaluation datasets with client SMEs, measuring ML or LLM quality, performing structured error analysis, grouping failures by root cause, and prioritizing improvements across releases.
Demonstrated production ownership after go-live, including monitoring, incident diagnosis, rollback, stabilization, performance and latency troubleshooting, and resolution of user-reported issues.
Ability to measure user adoption and business impact after launch using usage data and agreed KPIs such as time saved, error reduction, straight-through processing, quality improvement, or productivity gains.
Willingness and ability to travel or work onsite with client teams when an engagement requires

Work arrangement
Yes