Senior AI Engineer

About The Team / Organization
Outscale Partners is a fully owned subsidiary of The Argenbright Group and was established in 2022. The group brings more than 45 years of experience in transforming labour-intensive, essential frontline services across industries. It has operations across the United States, the United Kingdom, Middle East and India, supporting clients through a strong combination of industry expertise, technology, and people-centric service models.
Outscale Partners focuses on delivering innovative workforce and service solutions that enhance operational efficiency and customer experience.
Work Mode: Hybrid
Cab Facility: Company-provided cab for pick-up and drop within 50kms radius of the office ( As per company Policy)
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.
Must have skills
RAG pipelines
LLM orchestration frameworks
vector databases
Python
prompt engineering

Work arrangement
Yes