AI Engineer - Unifi

Engati Technologies Inc.; Gurgaon; Hybrid; INR 2,008,800 - 3,348,000; Full time

Benefits offered by this job

High visibility role

Access to senior leadership

Opportunities for real business impact

Job summary

Engati Technologies Inc. is looking for an AI Engineer to design, build, and operate AI systems across enterprise platforms. The role requires 7-8 years of software engineering experience, including 4-5 years in AI/LLM systems. Key responsibilities include collaborating with leadership to implement AI-driven capabilities using leading LLM platforms. The position offers a hybrid working arrangement, fostering collaboration and flexibility while ensuring impactful business solutions.

Qualifications

• 7 to 8 years of overall software engineering experience.
• 3 to 4 years of hands-on work on production AI/LLM systems.
• Experience integrating with major LLM providers.

Responsibilities

• Design, build, deploy, and operate AI systems across enterprise platforms.
• Collaborate closely with leadership and engineering teams.
• Develop and orchestrate AI agents capable of executing multi-step processes.

Skills

Proficiency in Python

Experience with vector databases

Understanding of prompt engineering

Experience in LLM orchestration frameworks

Deployment experience with Docker and Kubernetes

Tools

Docker

Kubernetes

Qdrant

FAISS

LangChain

Job description

Location: Hybrid | Experience: 7–8 Years | Experience in AI: 4–5 Years | Openings: 2

Outscale Partners (formerly AMH Services) is a Gurgaon, India-based global capability center (GCC) and strategic business solutions partner for the Argenbright Group, founded in 2022. It offers AI-enabled services in HR, finance, IT, legal, and customer support, focusing on rapid, scalable growth for enterprises

Role Summary

Reporting to the Senior AI Consultant, the AI Engineer will design, build, deploy, and operate AI systems across Unifi Service’s enterprise platforms.

The role spans the entire AI lifecycle — use‑case identification, model and system design, production deployment, monitoring, optimization, and continuous improvement.

Core Responsibilities

• Build and integrate AI-driven capabilities into enterprise applications and workflows using leading LLM platforms.
• Design and implement Retrieval‑Augmented Generation (RAG) architectures to enable AI systems to securely leverage internal knowledge and data sources.
• Develop and orchestrate AI agents capable of executing multi‑step, decision‑based business processes.
• Own production AI systems end‑to‑end, including deployment, versioning, monitoring, scaling, and cost optimization.
• Define, implement, and maintain evaluation metrics for AI quality, reliability, latency, and cost efficiency.
• Collaborate closely with Leadership, Product and Engineering teams to identify, prioritize, and deliver high‑impact AI solutions.
• Ensure AI solutions follow enterprise standards for security, data privacy, reliability, and maintainability.

Must‑Have (Non‑Negotiable) Skills & Experience

• 7 to 8 years of overall software engineering experience, with 3 to 4 years of hands‑on work on production AI / LLM systems.
• Strong proficiency in Python, with experience writing production‑quality, testable, and maintainable code.
• Proven experience designing and implementing RAG pipelines, including document ingestion, embeddings, retrieval, and response generation.
• Hands‑on experience with vector databases (e.g., Qdrant, FAISS, ChromaDb, or similar).
• Practical experience using LLM orchestration frameworks such as LangChain, LlamaIndex, Autogen, Haystack, or Semantic Kernel.
• Prior experience building AI solutions in SaaS or enterprise‑scale software environments.
• Experience integrating with major LLM providers such as OpenAI, Anthropic Claude, or Google Gemini.
• Solid understanding of prompt engineering, context engineering (context window management), and output control techniques.
• Experience deploying AI systems to cloud environments (AWS, Azure, or GCP) using Docker and Kubernetes.
• Working knowledge of LLMOps & MLOps practices, including model versioning, CI/CD, monitoring, and rollback strategies.
• Experience in implementing guardrails in AI Solutions along with Observability.

Preferred / Nice‑to‑Have Qualifications

• Exposure to multimodal AI systems, model fine‑tuning, or reinforcement learning from human feedback (RLHF).
• Familiarity with Model Context Protocol (MCP).
• Understanding of AI cost optimization, latency tuning, and performance benchmarking in production.
• Experience with domains such as eCommerce, Retail, HR, Finance, Legal, Compliance, etc.

Language & Documentation Expectations

• All production AI code must be clearly documented, version‑controlled, and supported by appropriate tests.
• AI pipelines, prompts, and agent workflows must include design documentation and usage guidelines.
• Each production AI system must have defined ownership, monitoring dashboards, and operational runbooks.
• Clear documentation of model limitations, assumptions, and fallback behaviours is mandatory.

What We Offer

• High‑visibility role with direct access to senior leadership and decision‑makers.
• Opportunity to design and own enterprise‑scale AI systems with real business impact.
• Hybrid work arrangement, balancing flexibility with collaboration.