Forward Deployed Engineer

Role - Forward Deployed Engineer

Location - NJ/NYC/ Charlotte/Columbus OH/ Pittsburg/Boston/ Malvern PA/Washington DC (onsite)

Job type- Full Time Employment

Exp-10+Years

Job description below

Must have - (Python/PySpark + Claude + SQL + APIs + Data & MLOps)

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 close collaboration and accelerated delivery.

Roles & Responsibilities

Design, develop, and deploy enterprise-grade AI and data applications using Python, PySpark, Claude, SQL, APIs, and modern cloud-based data platforms.

Engage with business and technology stakeholders to understand business objectives, data landscape, functional requirements, non-functional requirements, and delivery expectations.

Define solution architecture, implementation roadmaps, and technical approaches that align with business goals and operational requirements.

Architect scalable data platforms, data pipelines, and AI solutions with a strong focus on performance, security, reliability, and cost optimization.

Develop batch, real-time, and streaming data processing solutions integrating multiple enterprise data sources and downstream applications.

Build and operationalize Claude-powered applications, copilots, and AI workflows using prompt engineering, Retrieval-Augmented Generation (RAG), vector search, and enterprise data integration.

Implement responsible AI practices, including governance, security, privacy, traceability, validation, and human oversight controls.

Design and enforce engineering standards, reusable frameworks, CI/CD practices, infrastructure automation, and production deployment patterns.

Provide technical leadership across solution design, code reviews, architecture reviews, testing, deployment, and production support activities.

Drive platform reliability, performance tuning, observability, incident resolution, and operational excellence across data and AI workloads.

Collaborate closely with architects, data engineers, AI specialists, platform teams, and client stakeholders to deliver production-ready solutions.

Support solutioning, RFP responses, technical demonstrations, effort estimation, and client workshops for new opportunities.

Develop reusable accelerators, reference architectures, implementation playbooks, and engineering best practices to improve delivery efficiency.

Mentor engineering teams and contribute to hiring, capability development, technical assessments, and knowledge-sharing initiatives.

Embed with client SMEs, product owners, and engineers for a defined, time-bound outcome; map the workflow, users, data, constraints, and decision points; and agree on the first useful release and measurable success criteria.

Analyze client data early to assess coverage, quality, volumes, distributions, edge cases, and process feasibility before committing to solution design and build scope.

Build a thin working version quickly on real data and APIs, validate it with business users, and harden it iteratively for security, reliability, maintainability, and production operations.

Create evaluation datasets with client SMEs, perform model and LLM error analysis. and suggest improvements.

Resolve data-access, API, identity, network, security, infrastructure, and deployment blockers in partnership with client data, platform, security, and engineering te ams.

Own post-go-live stabilization by monitoring services, diagnosing user-reported failures, latency and cost issues, executing rollback or recovery where required, and shipping fixes.

Measure adoption and realized business impact using usage data and agreed KPIs, and communicate results through concise dashboards, demonstrations, or executive summaries.