Senior Forward Deployed Engineer – AI & Data Science

Senior Forward Deployed Engineer – AI & Data Science

Company: Arise Tech Global

Experience: 8–14 years, depending on educational qualification

Location: Hyderabad, India

Employment Type: Full-time

About Arise Tech Global

Arise Tech Global is a technology and talent solutions company helping organizations build high-performing teams across enterprise technology, infrastructure, cloud, cybersecurity, and digital transformation.

About the Role

We are looking for a Senior Forward Deployed Engineer to join an innovative AI and Data Science engineering team. This role focuses on the end-to-end technical delivery of complex AI and automation solutions, from business discovery and solution design to development, evaluation, production deployment, and stabilization.

The ideal candidate will combine hands-on engineering expertise with technical leadership, working across Applied AI/ML, Generative AI, RAG, AI agents, enterprise integrations, and MLOps/LLMOps. The role requires the ability to translate complex business problems into scalable, secure, production-ready solutions while coordinating multidisciplinary teams and delivering measurable business value.

Key Responsibilities

• Lead technical discovery by understanding business workflows, user needs, success criteria, data readiness, integration dependencies, and production constraints.
• Translate business challenges into executable solution designs, delivery plans, technical workstreams, estimates, milestones, and acceptance criteria.
• Build, prototype, review, and contribute to critical production components, including AI-enabled applications, RAG systems, bounded agents, intelligent automation, APIs, and integrations.
• Define end-to-end architecture across applications, workflows, data pipelines, models, retrieval systems, agents, enterprise integrations, identity, access controls, and observability.
• Coordinate delivery across engineering, data science, ML, testing, platform, security, compliance, and business teams.
• Establish integrated testing, AI evaluation, and governance processes covering functionality, performance, security, model quality, retrieval, generation, tool usage, and human oversight.
• Coordinate production readiness through CI/CD, staged releases, monitoring, logging, service-level objectives, rollback mechanisms, recovery procedures, and runbooks.
• Support production issue triage, early stabilization, and transition to operational teams.
• Communicate technical evidence, risks, trade-offs, and delivery progress to stakeholders.
• Measure solution adoption and business value, translating delivery insights into reusable components, accelerators, standards, documentation, and playbooks.

Required Skills & Qualifications

Education & Experience

Candidates must meet one of the following qualification and experience criteria:

• Doctorate in Computer Science, Information Technology, or a related field, with 1+ year of relevant experience.
• Master’s degree in Computer Science, Information Technology, or a related field, with 8–10 years of experience.
• Bachelor’s degree in Computer Science, Information Technology, or a related field, with 10–12 years of experience.
• Diploma in a related field, with 12–14 years of experience.

Technical Skills

• Demonstrated end-to-end technical ownership of at least one production AI, ML, software, data, or automation solution that delivered measurable enterprise outcomes.
• Strong hands-on proficiency in Python and SQL.
• Experience designing or reviewing production software, APIs, services, data flows, evaluation pipelines, and enterprise integrations.
• Ability to translate complex business problems into technical designs, executable delivery plans, acceptance criteria, and production-readiness evidence.
• Advanced expertise in at least one of the following areas, with credible breadth across the production lifecycle:
• Applied AI/ML
• Generative AI, RAG, and AI agents
• Full-stack and integration engineering
• AI platforms and MLOps
• Strong understanding of enterprise solution architecture, cloud-native engineering, DevSecOps, and production operations.
• Familiarity with model evaluation, Responsible AI, privacy, governance, auditability, and regulated delivery requirements.

Preferred Skills

• Advanced RAG, hybrid or graph retrieval, knowledge graphs, source verification, MCP-style integrations, and multi-agent workflows.
• Experience with AWS, Amazon Bedr…