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Principal Software Consultant - AI/ML Engineer

Pakistan, Lahore, Karachi, Islamabad · Job Posted May 17, 2026
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Job Description

As an ML Team Lead, you will be responsible for leading the technical direction of AI/ML initiatives while remaining deeply hands-on in building scalable production-grade ML and LLM systems. This is a highly technical leadership role where approximately 70% of the time will be spent designing systems, writing production code, conducting design and code reviews, debugging production issues, and shipping ML/AI solutions, while 30% will focus on technical leadership, mentoring engineers, and collaborating with product and engineering leadership on roadmap planning. You will play a critical role in architecting and scaling AI/ML systems including classical machine learning models, Retrieval-Augmented Generation (RAG) pipelines, multi-step agentic systems, and LLM-powered applications. You will also establish engineering standards around scalability, security, resilience, observability, cost optimization, and production readiness for all AI systems built by the team.

Job Responsibility

  • Lead the technical direction for the team’s ML and LLM systems, including architecture patterns, platform choices, evaluation frameworks, and engineering standards
  • Stay hands-on by designing and implementing complex ML and agentic AI systems, writing production-grade code, and leading through technical execution
  • Design, develop, and deploy scalable ML and LLM-powered applications and services in production environments
  • Build and optimize AI-powered solutions such as RAG systems, multi-step agents, AI assistants, chatbots, forecasting systems, ranking models, classification models, and optimization systems
  • Drive architecture and design reviews to ensure scalability, reliability, security, and maintainability of AI/ML systems
  • Own the technical roadmap for ML/LLM initiatives and translate business objectives into execution plans and scalable solutions
  • Collaborate closely with Product Managers, Engineers, Data Engineers, MLOps Engineers, QA Engineers, and cross-functional stakeholders to deliver business-aligned AI solutions
  • Establish engineering best practices for prompt engineering, model evaluation, regression testing, observability, and production readiness
  • Define and implement quality standards, evaluation suites, acceptance metrics, and regression plans for all AI/ML features
  • Ensure high availability, scalability, and resilience of tier-1 ML services through SLOs, monitoring, incident response, failover strategies, circuit breakers, and multi-zone deployments
  • Drive security and safety best practices for AI systems, including handling of untrusted inputs, prompt injection prevention, secure tool usage, PII protection, and internal security reviews
  • Optimize infrastructure, token, GPU, and operational costs by making cost efficiency a first-class design consideration
  • Mentor and grow ML, GenAI, Data, MLOps, and QA engineers through technical guidance, design discussions, code reviews, pairing sessions, and growth planning
  • Partner with the MLOps team on platform strategy and collaborate with Data Engineering teams to build scalable data foundations for ML systems
  • Participate in architecture councils, product reviews, engineering planning sessions, and security reviews as the representative of the engineering team
  • Support hiring efforts by interviewing, calibrating, onboarding, and mentoring new engineers
  • Stay updated with emerging trends and advancements in AI/ML, LLMs, cloud platforms, MLOps, and agentic AI frameworks

Requirements

  • Bachelor's or Master's degree in computer science, Artificial Intelligence, Data Science, Software Engineering, or a related field
  • 7+ years of professional software engineering experience with at least 5 years of hands-on experience building and deploying ML systems into production
  • Prior experience as a Tech Lead, Staff Engineer, or hands-on lead for AI/ML engineering teams
  • Strong expertise in classical machine learning domains such as forecasting, ranking, classification, and optimization
  • Hands-on experience building modern LLM and agentic AI systems including RAG pipelines, tool-using agents, multi-step workflows, and evaluation systems
  • Strong proficiency in Python and backend system development
  • Experience with ML frameworks such as PyTorch or TensorFlow
  • Strong understanding of scalable distributed systems, APIs, system integration, architecture design, and production engineering practices
  • Experience operating ML services at scale, including SLO management, monitoring, on-call practices, and incident response
  • Experience working with Kubernetes-based deployments, CI/CD pipelines, and modern cloud-native engineering practices
  • Production experience with cloud platforms such as Azure, AWS, or GCP, preferably Azure and AKS
  • Hands-on experience with vector databases, semantic search systems, and LLM evaluation methodologies
  • Experience with prompt engineering, model evaluation, regression testing, and AI system optimization
  • Strong understanding of system reliability, resiliency patterns, observability, scalability, and performance optimization
  • Experience working with modern data warehouses and distributed data systems
  • Strong leadership, mentoring, stakeholder management, analytical, and problem-solving skills
  • Excellent written and verbal communication skills in English
  • Ability to collaborate effectively with technical and non-technical stakeholders across distributed teams

Nice to have

  • Experience in supply chain, logistics, warehousing, or e-commerce domains
  • Experience scaling AI/ML engineering teams and mentoring engineers across multiple disciplines
  • Experience with Azure AI Foundry, AWS Bedrock / AgentCore, Amazon SageMaker, or Vertex AI
  • Experience driving security and compliance reviews for AI/ML systems
  • Open-source contributions, conference speaking engagements, or published technical writing

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