Lead AI Integration Engineer

Key Responsibilities

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Design, build, and deploy intelligent backend services using Python and Azure AI Foundry.

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Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction.

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Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users.

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Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow.

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Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model.

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Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution.

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Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value.

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Mentor junior engineers and champion secure, responsible AI coding practices within your team.

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Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage.

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Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage.

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Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements.

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Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design.

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Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery.

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Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality.

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Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

Minimum Qualifications

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A minimum of 8+ years of experience in software engineering, with at least 2+ years of hands-on experience integrating LLMs and building AI-driven applications.

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Demonstrated experience architecting and deploying LLM-based or agentic systems in production, including prompt design, retrieval architecture, and tool/function calling (e.g., MCP)

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Hands-on experience with leading LLMs and APIs, including OpenAI, Google Gemini, and Anthropic Claude.

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Experience evaluating and selecting open-source and proprietary LLMs (e.g., OpenAI via Azure AI Foundry) based on accuracy, latency, scalability, cost, and overall performance for production AI solutions.

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Knowledge of vector stores, embeddings (LangChain, LlamaIndex, etc.).

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High level of English proficiency required to interact with a globally-based development team and client stakeholders.

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Deep expertise in Python for backend services and AI integration (replacing traditional Java/C# requirements).

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Demonstrated, hands-on experience utilizing Azure AI Foundry to build, deploy, and manage AI models and agents.

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Proven track record of building and managing complex API integrations for data ingestion, extraction, and classification.

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Strong understanding of security engineering, specifically implementing PII redaction, anonymization pipelines, and secure data handling in AI workflows.

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Experience delivering well-tested, scalable, secure, and performant enterprise-level systems that achieve client business outcomes.

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Strong track record of working within Agile software development frameworks.

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Proficiency with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) as a core daily practice, including pipeline construction, test generation, and documentation.

Additional Experience Desired

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• Strong programming skills in Python, and familiarity with libraries such as PyTorch, TensorFlow, and Scikit-Learn.
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Exposure to Matplotlib, Seaborn, or Plotly

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• Familiarity with building and operating Cloud Native applications, including infrastructure-as-code and observability practices.
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• Experience with at least one cloud platform and CI/CD for ML (GitHub Actions, MLflow, etc.).
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• Familiarity with emerging AI Regulations (e.g., EU AI Act, NIST AI RMF) and practical implementation of AI safety guardrails.

Benefits

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• Medical Insurance benefits as per company policy.
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• Life Insurance as per company policy
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• 15 days of paid vacation, sick leave and paid holidays as per local law
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• Paternity and maternity leave as per as per local law
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• Marriage, bereavement and graduation leaves as per company policy
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• Sick leave and paid holidays as per local law
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• Christmas and Middle year bonuses as per local law
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• Monthly productivity bonus as per local law
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• Discounts in local shops
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• Direct deposit of payroll.
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• Paid professional certifications
• What It's Like to Work at 3Pillar
• At 3Pillar, we create an environment where people can do their best work while maintaining a healthy work-life balance.
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• Flexibility & Well-being – Our remote-first approach gives you the flexibility to work where you perform best, while prioritizing your well-being and personal commitments.
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• Global Community – Collaborate with talented colleagues across the globe in a culture built on connection, support, and shared success.
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• Your Voice Matters – We foster open communication and multiple feedback channels, ensuring every employee has the opportunity to be heard and make an impact.
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• Growth & Development – Gain exposure to diverse clients, industries, and challenges that accelerate learning and career growth.
• Our culture is guided by four core values: Collaboration, Outperform, Respect, and Evolve—the principles that shape how we work, grow, and succeed together.