Forward Deployed Software Engineer Axtria Hyderabad Bengaluru Delhi Ncr

Vibehackers; Delhi; On-site; INR 250,000 - 450,000; Full time

Job summary

Vibehackers embeds within pharmaceutical client organizations to architect, build, and deploy production-grade AI systems on client infrastructure. You will lead technical workstreams and senior relationships, ensuring solutions are compliant, observable, and domain-defensible for pharma use cases.

You will direct engineering teams, own end-to-end design and deployment, and integrate with platforms like Databricks, Snowflake, AWS Bedrock, and Claude APIs.

Qualifications

• Proven experience architecting production AI/ML systems, preferably in regulated environments.
• Experience leading technical teams and managing senior client/stakeholder relationships.
• Familiarity with multi-agent AI systems and production MLOps practices.
• Practical experience integrating with enterprise data stacks and large-model APIs.

Responsibilities

• Operate as a trusted technical peer embedded in client teams and own architecture and delivery decisions.
• Lead and direct teams of engineers under your architecture and delivery ownership.
• Scope problems with senior client stakeholders and translate technical outcomes into business language.
• Architect multi-agent AI systems with RAG, memory architectures, and production governance.

Skills

AI Architecture

Production MLOps

Multi-agent Systems

Regulatory Compliance

Observability

Team Leadership

Client Engagement

Pharma Domain

Job description

Builds agentic AI systems and integrates AI assistant APIs (Claude/Anthropic, Bedrock) — directly tied to vibe-style agent/assistant development.

About the Role

Embed within pharma client organizations to architect, build, and deploy production-grade AI systems on client infrastructure. Lead technical workstreams and teams, manage senior technical relationships, and ensure AI solutions are compliant, observable, and domain-defensible for pharma use cases.

Job Description

Role

Embed directly inside pharmaceutical client organizations as a Forward Deployed Software Engineer to own the end-to-end design, build, and deployment of production AI systems on the client’s infrastructure. Lead technical workstreams and direct engineering teams while holding senior technical relationships with Directors, VPs, and CDOs.

Key Responsibilities

• Operate as a trusted technical peer embedded in client teams and own architecture and delivery decisions.
• Lead and direct teams of engineers (Agent Pipeline Engineers, AI-Augmented Engineers) under your architecture and delivery ownership.
• Scope problems with senior client stakeholders, present architecture trade-offs, defend design decisions, and translate technical outcomes into business language.
• Architect multi-agent AI systems, including orchestration patterns, tool/function integration, retrieval-augmented generation, memory architectures, human-in-the-loop design, evaluation pipelines, and production MLOps.
• Build on clients’ existing technology stacks and integrate with platforms such as Databricks, Snowflake, AWS, and Anthropic/Claude APIs.
• Design and implement AI evaluation frameworks suitable for regulated pharma environments (probabilistic quality thresholds, RAGAS, LLM-as-judge, adversarial red-teaming, audit-trail compliant governance).
• Ensure systems are production-grade: observable, maintainable, secure, and compliant with pharma data governance requirements (including HIPAA, GDPR, and applicable FDA AI/ML guidance).
• Stay current with the agentic AI ecosystem and translate emerging capabilities into deployment-relevant technical decisions.
• Translate pharma commercial and clinical problems into AI-solvable architectures and validate outputs against pharma business logic and norms.

Technical Scope & Tech Stack (examples mentioned)

• Databricks (Delta Lake, Mosaic AI, Genie)
• Snowflake (Snowpark, Cortex AI, Cortex Analyst)
• AWS (Bedrock Agents, SageMaker)
• Claude and Anthropic API stack with Model Context Protocol
• Retrieval-augmented generation (RAG), multi-agent systems, production MLOps, evaluation and red-teaming frameworks

Compliance & Governance

• Design solutions that meet pharma-specific data governance and regulatory expectations (HIPAA, GDPR, FDA guidance).
• Implement audit-trail-compliant output governance and domain-defensible validation for clinical and commercial outputs.

Requirements

• Proven experience architecting and delivering production AI/ML systems, preferably in regulated environments.
• Experience leading technical teams and managing senior client/stakeholder relationships.
• Familiarity with multi-agent AI systems, RAG patterns, model evaluation frameworks, and production MLOps practices.
• Practical experience integrating with Databricks, Snowflake, AWS ML/agent services, and large-model API providers (Anthropic/Claude or similar).
• Strong pharma domain understanding sufficient to translate business problems into technical solutions and validate domain-defensible outputs.
• Focus on observability, maintainability, security, and regulatory compliance in system design.

Skills

AI Architecture System Design Production MLOps Multi-agent Systems Retrieval-augmented Generation Evaluation & Testing Adversarial Red-Teaming Observability Security Regulatory Compliance Pharma Domain Knowledge Client Engagement Stakeholder Management Technical Leadership Technical Communication Delivery Ownership