Consultant - AI Engineer

COMPANY OVERVIEW

The Argenbright Group - Argenbright Holdings is a privately held provider of workforce solutions in human-capital intensive industries, headquartered in Atlanta, Georgia. With over four decades of experience across security, aviation services, and facilities management, the Group is driven by a mission to deliver legendary service through strong processes, personal attention, and scalable global operations.

Founded in 1979 by Mr. Frank A. Argenbright Jr., the Group has built and scaled several successful companies including AHL Services, AirServ, and SecurAmerica. Collectively, Argenbright portfolio companies have generated employment for more than 100,000 people across the US and Europe. Today, the Group continues to invest strategically in aviation, security, and facilities services to positively impact the lives of frontline workers.

Outscale Partners India Private Limited (https://outscalepartners.com) - Outscale Partners India Private Limited (part of The Argenbright Group) was founded to redefine outsourcing for modern enterprises. Headquartered in Gurugram, India, Outscale operates a secure Level 5 delivery center with 24/7 operations.

Outscale provides access to top-tier talent, deep process expertise, and automation-led solutions that enable clients to scale confidently. More than a traditional service provider, Outscale functions as an extension of client teams, combining the trust of a captive model with the flexibility and scale of a third-party organization.

Position Title : Consultant - AI Engineer

Position Type : Full-time

Shift : 12:30 PM to 9:30 PM IST

Work Location : Sector – 49, Gurugram, Haryana

Work Mode : Onsite

Relevant Experience : 3-4 years (Overall 7 to 8 years)

JOB DESCRIPTION

Role Summary : Reporting to the Senior AI Consultant, the AI Engineer will design, build, deploy, and operate AI systems across Unifi Services enterprise platforms.

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

KEY 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.

• Experience in implementing guardrails in AI Solutions along with Observability.

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.

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

Must have skills

• RAG pipelines

• LLM orchestration frameworks

• vector databases

• Python

• prompt engineering