Lead Machine Learning Engineer

Acrobit is seeking a highly skilled Lead Machine Learning Engineer to join its on-site team in Lake City, Lahore. This senior-level role demands a hands-on engineer with extensive experience in building, deploying, and managing production-grade machine learning and Generative AI systems. The successful candidate will lead the design and delivery of scalable ML solutions, AI agents, LLM applications, and AWS-based AI infrastructure. Collaboration with global clients and development teams is a key aspect of this position, requiring strong technical leadership and communication skills.

Key Responsibilities

- Architect, develop, and deploy production-grade machine learning systems.
- Lead the development of Agentic AI, Generative AI, and LLM-powered applications.
- Build AI agents utilizing tool calling, function calling, memory, orchestration, and multi-step workflows.
- Design and implement Retrieval-Augmented Generation (RAG) systems, knowledge bases, and enterprise search solutions.
- Develop secure and scalable ML APIs and backend services primarily using Python.
- Manage model training and deployment workflows with AWS SageMaker.
- Implement SageMaker Pipelines, model registries, monitoring, and lifecycle management.
- Develop AI applications leveraging AWS Bedrock foundation models and APIs.
- Build Bedrock agents, knowledge bases, guardrails, and model integrations.
- Establish monitoring, observability, evaluation, and performance tracking for AI systems.
- Work with both structured and unstructured data using SQL and modern data platforms.
- Review architecture, code quality, security, and system performance.
- Mentor engineers and provide technical leadership across AI and ML projects.
- Participate in client meetings, technical discussions, and solution planning.
- Translate business requirements into practical, scalable technical solutions.

Required Qualifications

- Minimum of 8 years of professional experience in software or machine learning engineering.
- Strong hands-on engineering expertise rather than purely research or data science background.
- Advanced proficiency in Python and SQL.
- Proven track record of architecting, developing, and deploying ML models in production environments.
- Extensive experience with Agentic AI, Generative AI, and Large Language Models (LLMs).
- Practical knowledge of building AI agents, agent workflows, RAG pipelines, memory systems, and tool integrations.
- Experience with LLM evaluation, monitoring, observability, and prompt management.
- Solid understanding of APIs, microservices, cloud architecture, and production deployment.
- Demonstrated leadership skills in guiding engineers and making architectural decisions.
- Strong English communication skills for effective collaboration with international clients.

Mandatory AWS Experience

Candidates must have hands-on production experience with:

- AWS SageMaker: model development, training, real-time and batch deployment, Pipelines, Model Registry, monitoring, endpoint management, and ML lifecycle automation.
- AWS Bedrock: foundation model integration, Bedrock APIs, Bedrock Agents, knowledge bases, RAG, guardrails, model evaluation, and secure enterprise AI integration.

Preferred Qualifications

- Experience with C++ is advantageous.
- Familiarity with orchestration frameworks such as LangChain or LangGraph.
- Knowledge of vector databases and embedding-based retrieval techniques.
- Experience with containerization and orchestration tools like Docker and Kubernetes, as well as CI/CD pipelines.
- Understanding of MLOps, model governance, and infrastructure automation.
- Prior experience working with US-based or other international clients.

Additional Role Details

This is a full-time, on-site position based in Lake City, Lahore. Working hours are from 5:00 PM to 2:00 AM Pakistan Standard Time, aligned with the EST business schedule. Remote or part-time work arrangements are not available for this role.

Candidates should be prepared to discuss their professional experience, specific AWS SageMaker and Bedrock projects, AI agent development, and availability to work on-site during the specified hours. Salary expectations and notice period will also be part of the screening process.