{"id":3459430,"url":"https://alion.io/job/seismic-consulting-senior-ai-engineer","title":"SENIOR AI ENGINEER","company":{"id":1379660,"name":"Seismic Consulting","domain":"seismicconsulting.ca","url":"https://alion.io/company/seismicconsulting","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"C","score":62,"open_postings":19,"ghost_share":0.632,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Abuja, Nigeria"],"countries":["NG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16000,"max_usd":42000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"AutoGPT","optional":true},{"name":"Kubeflow","optional":true},{"name":"LangGraph","optional":true},{"name":"MLFlow","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"RAG","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2025-12-27T00:00:00Z","employer_posted_date":"2025-12-27","last_verified_at":"2026-10-31T16:00:59Z","board_verified":true,"closed_at":null,"days_open":308,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":308},"description":"We are seeking a Senior AI Engineer with expertise in AI agent development to design, build, and deploy intelligent agents that operate autonomously in digital environments. This role involves creating AI agents capable of advanced reasoning, planning, decision-making, and interactive tasks using large language models (LLMs), multi-agent systems, and simulation frameworks. You will work at the frontier of AI development, building agents that learn from data, communicate with users or other systems, and adapt to complex, dynamic environments. In addition, you will championMLOps and LLMOps best practices to ensure our AI applications and models are robustly trained, deployed, and maintained at scale.\nIn your role, you would be required to \nDesign and implement AI agents using LLMs, planning algorithms, and decision-making frameworks.\nDevelop agent architectures that support autonomy, interactivity, and effective task completion.\nIntegrate agents into applications, APIs, or workflows (e.g., chatbots, copilots, automation tools) to deliver user-facing functionality.\nImplement MLOps and LLMOps (Large Language Model Ops) best practices - including pipeline design, CI/CD for model deployment, and performance monitoring - to ensure scalable and reliable AI agent operations.\nLead strategic initiatives related to the model lifecycle, such as automated retraining, model versioning, and deployment workflows, to continuously improve model performance and reliability.\nCollaborate with researchers, engineers, and product teams to iterate on agent capabilities and ensure alignment with product goals.\nOptimize agent behaviour through feedback loops, reinforcement learning techniques, or user interaction data to improve performance over time.\nMonitor agent performance, conduct evaluations, and implement safety/guardrail mechanisms to uphold reliability, ethical standards, and user trust.\nContribute to the architecture of large-scale systems, providing technical leadership and influencing product direction with AI-driven solutions.\nMaintain thorough documentation of agent logic, design decisions, pipelines, and dependencies for future reference and knowledge sharing.\nRequirements\nEducation: Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field.\nPython Development: 5+ years of hands-on software development experience, with strong software engineering skills in Python (including PySpark) and full lifecycle development (design, coding, testing, deployment).\nMachine Learning Expertise: 5+ years of experience developing and deploying machine learning models and workflows, with a solid understanding of ML principles, model training, fine-tuning, and evaluation.\nGenerative AI Leadership: Hands-on experience leading ML Generative AI initiatives from concept to deployment.\nAI/ML Frameworks: Strong programming skills in Python and experience with AI/ML frameworks and libraries such as LangChain, OpenAI API, Hugging Face, PyTorch, and scikit-learn.\nMLOps & LLMOps: Solid understanding and experience implementing MLOps and LLMOps practices, including automated ML pipelines, CI/CD for models, model monitoring, and lifecycle management.\nContainerization & Orchestration: Proven experience designing and deploying AI solutions using Docker and Kubernetes, with knowledge of scalable architectures.\nAgent-Based Systems: Understanding of agent-based modeling, reinforcement learning, multi-agent systems, and planning algorithms.\nLLM Applications: Experience building applications with large language models (LLMs) or generative AI, integrating them into real-world products and services.\nSystem Integration: Familiarity with API development, backend services, and deployment pipelines for ML models.\nWork Style: Ability to work independently in experimental, fast-paced environments, rapidly prototyping and iterating on ideas.\nPreferred Qualifications\nExperience with frameworks for autonomous agents (e.g., AutoGPT, LangChain, Langgraph Agents) and multi-agent system orchestration.\nBackground in human-AI interaction, conversational UX, or simulation environments that involve AI agents.\nKnowledge of vector databases, prompt engineering, or retrieval-augmented generation (RAG) to enhance LLM-driven agent performance.\nHands-on experience with MLOps and LLMOps tools and platforms (e.g., MLflow, TensorFlow Extended, or Kubeflow) and managing large-scale model deployments.\nContributions to open-source AI projects or a 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