Senior/Lead AI & ML Engineer

ABOUT THE COMPANY

Seismic Consulting Group operates at the intersection of public policy, data, and implementation to drive inclusive development across Africa. The organization partners with governments, international institutions, and mission-driven organizations to design and execute data-driven strategies that convert policy into measurable social and economic outcomes.

ROLE DESCRIPTION

We are building and scaling a portfolio of production AI systems that sits inside real, high-stakes workflows — spanning consumer-facing platforms and enterprise knowledge systems used by non-technical stakeholders across multiple markets. We're looking for a Senior/Lead AI/ML Engineer to take technical ownership of this portfolio: someone who has shipped machine learning and LLM-based systems to production, not just prototyped them, and who is ready to set technical direction while still writing code.

This is a hands-on leadership role. You will architect and build core AI capabilities yourself, while also mentoring engineers, setting engineering standards, and acting as the technical authority that leadership turns to when scaling decisions need to be made.

KEY ROLES AND RESPONSIBILITIES

AI/ML Architecture and Technical Strategy

• Define the technical architecture and roadmap for the company's AI/ML products and digital platforms.
• Lead model selection, fine-tuning, and prompting strategy across open- and closed-source large language models.
• Design retrieval-augmented generation, vector search, and knowledge-retrieval systems suited to production workloads.
• Set architectural standards that balance accuracy, latency, cost, and scalability.
• Assess emerging AI/ML techniques and tools for relevance and production readiness.

Engineering Delivery and Production Systems

• Own end-to-end delivery of AI/ML features from design through production deployment.
• Build and maintain model-serving, inference, and GPU/compute optimization pipelines.
• Ensure systems perform reliably under real user load, not only in prototype or demo conditions.
• Coordinate technical delivery timelines with product and operations teams.
• Troubleshoot and resolve production incidents affecting AI/ML systems.

MLOps, Infrastructure and Reliability

• Build CI/CD pipelines and containerized deployment workflows for ML systems.
• Implement monitoring, logging, and alerting for model and system performance.
• Manage cloud infrastructure and cost/performance optimization across environments.
• Establish infrastructure-as-code practices for reproducible environments.
• Maintain system uptime, scalability, and disaster-recovery readiness.

Data Engineering and Architecture

• Design data pipelines, storage architecture, and data models that support AI/ML systems at scale.
• Ensure data quality, validation, and governance across ingestion and processing workflows.
• Work with structured and unstructured data from multiple internal and external sources.
• Identify when data quality, rather than model choice, is the limiting factor in system performance.

Evaluation, Quality and Risk Management

• Build evaluation frameworks to measure accuracy, grounding, hallucination rates, and retrieval quality.
• Detect and address silent quality regressions during model, data, or index changes.
• Establish testing, benchmarking, and A/B evaluation practices for AI/ML systems.
• Uphold security, privacy, and compliance standards for systems handling sensitive user and organizational data.

Team Leadership and Talent Development

• Lead and coordinate the AI/ML engineering team, including work allocation and performance monitoring.
• Mentor junior and mid-level engineers and data scientists through code review and architectural guidance.
• Set engineering standards, best practices, and technical documentation expectations.
• Support the recruitment and onboarding of additional AI/ML engineering talent as the team scales.

Cross-Functional Collaboration and Stakeholder Engagement

• Translate business and product goals into realistic, sequenced technical plans.
• Collaborate with product managers, consultants, project managers, and executive leadership.
• Work with Finance, Legal, Operations, and Corporate Communications where AI/ML systems intersect with their functions.
• Present technical progress, risks, and recommendations to executive management.

Expected Deliverables

Within the First 90 Days

• Prepare a technical architecture assessment and roadmap for the company's priority AI/ML systems.

• Establish or strengthen evaluation frameworks for accuracy, grounding, and retrieval quality.

• Develop an MLOps and deployment framework, including CI/CD and monitoring standards.

• Identify and remediate critical production risks in existing AI/ML systems.

• Establish engineering standards, documentation practices, and code-review processes.

• Develop a data architecture and quality-improvement plan across priority systems.

• Onboard and begin mentoring junior and mid-level engineering team members.

WITHIN YOUR FIRST YEAR, WE EXPECT THAT YOU WOULD HAVE;

• Delivered production-grade AI/ML systems that perform reliably at scale across priority platforms.

• Reduce hallucination, latency, and quality-regression incidents through systematic evaluation.

• Established a scalable MLOps infrastructure supporting rapid, reliable releases.

• Built and developed a capable AI/ML engineering team with clear technical standards.

• Strengthened data pipelines and architecture supporting cross-platform AI capabilities.

• Established security, privacy, and compliance practices appropriate to sensitive-data systems.

Key Performance Indicators

Technical Delivery

• On-time delivery of AI/ML features and system releases.

• Number and severity of production incidents affecting AI/ML systems.

• System uptime, latency, and reliability metrics.

System Performance and Quality

• Accuracy, grounding, and retrieval-quality metrics across evaluation frameworks.

• Reduction in hallucination rates and silent quality regressions.

• Model and infrastructure cost efficiency relative to performance

Data and Evaluation

• Data quality and pipeline reliability metrics.

• Coverage and consistency of automated evaluation and testing.

• Frequency and rigor of benchmarking across model and system changes.

Team and Leadership

• Growth and performance of AI/ML engineering team members.

• Quality and consistency of technical mentorship and code review.

• Team retention and engineering capability development.

Cross-Functional and Stakeholder Performance

• Quality and timeliness of technical communication to executive management.

• Effectiveness of collaboration with product, operations, and other functions.

• Stakeholder confidence in technical roadmap and delivery commitments.

Educational Qualifications

• A bachelor's degree in Computer Science, Data Science, Software Engineering, Statistics, Information Technology or a related discipline.

• A master's degree in Computer Science, Artificial Intelligence, Data Science or a related field is desirable.

• Professional certifications in cloud infrastructure, machine learning, MLOps, or data engineering would be an advantage.

Professional Experience

• A minimum of 8 years of relevant experience in AI/ML engineering, software engineering, or applied data science, including significant hands-on experience shipping AI/ML or LLM-based systems to production.

• Demonstrated experience with retrieval-augmented generation, vector databases, prompt engineering, fine-tuning, and model evaluation.

• Strong understanding of transformer internals and inference-time behavior sufficient to make sound architecture and cost decisions.

• Proven experience with cloud infrastructure, containerization, orchestration, and MLOps practices.

• Demonstrated experience with data engineering at scale, including pipelines, warehousing, and data quality management.

• Experience leading or mentoring engineering or data science teams.

• Experience operating in regulated or sensitive-data contexts, such as healthcare, financial services, or government systems, is a strong advantage.

• Demonstrated ability to communicate technical tradeoffs to non-technical executives and translate business priorities into engineering plans.

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