Forward Deployment Engineer

Role Name: AI/ML & Forward Deployed Engineer (8+ Years)

Role Overview

We are looking for an experienced AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver high-impact AI/ML (and GenAI, where applicable) solutions end-to-end. You will blend applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade systems that are scalable, secure, observable, and aligned to business KPIs.

This role is ideal for engineers who enjoy operating at the intersection of data + models + systems + real users, and who can thrive in ambiguous, fast-moving environments

Key Responsibilities

1) Use-Case Discovery & Forward Deployment

• Partner with stakeholders (business/product/customers) to identify and shape AI opportunities into well-defined use cases with success metrics, constraints, and rollout plans.

• Run workshops and technical discovery to assess feasibility, data readiness, integration needs, and operational risks.

• Drive rapid prototyping, pilot deployments, and iterative improvements based on real user feedback.

2) Applied ML Engineering (Classic ML + Deep Learning)

• Develop and improve ML solutions (classification, regression, ranking, forecasting, anomaly detection, NLP).

• Establish and maintain robust evaluation practices: offline metrics, validation strategies, experimentation, and A/B testing.

• Perform feature engineering, error analysis, model optimization, and performance tuning for production requirements.

3) GenAI / LLM Engineering (If Applicable)

• Build and product ionize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategy, embeddings, retrieval tuning, reranking, and response grounding.

• Implement guardrails and reliability patterns: prompt templates, tool/function calling, hallucination reduction, citation strategies, and fallback paths.

• Develop evaluation harnesses for GenAI: quality metrics, regression tests, safety tests, and human-in-the-loop workflows.

4) Productionization (MLOps / LLMOps)

• Package models into scalable services and deploy using Docker/Kubernetes and CI/CD.

• Implement model lifecycle management: model registry, versioning, automated retraining triggers, and governance workflows.

• Build monitoring and observability: drift detection, latency/throughput monitoring, error tracking, alerting, and rollback mechanisms.

5) Systems Integration & Platform Collaboration

• Build integration layers (REST/gRPC APIs, event-driven services) to embed AI capabilities into products and enterprise workflows.

• Collaborate with data engineers to design reliable pipelines and ensure data quality, lineage, and governance.

• Ensure secure and compliant design (PII/PHI handling, RBAC, secrets management, encryption, audit trails).

6) Technical Leadership & Enablement

• Provide technical guidance and mentoring to engineers; lead design reviews and establish best practices.

• Document solutions with architecture diagrams, runbooks, and operational playbooks.

• Create reusable accelerators (templates, libraries, patterns) to scale deployments across teams or customers.

Salary Range: $100,000- $120,000 a year

TCS Employee Benefits

Summary:

Discretionary Annual Incentive.

Comprehensive Medical Coverage:
Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.

Family Support:
Maternal & Parental Leaves.

Insurance Options:
Auto & Home Insurance, Identity Theft Protection.

Convenience & Professional Growth:
Commuter Benefits & Certification & amp;
Training Reimbursement.

Time Off:
Vacation, Time Off, Sick Leave & Holidays.

Legal & Financial Assistance:
Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

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