AI Engineer

Location: Remote from Algeria. MUST submit a Resume in English. Work hours are in the US EST time zone. The Opportunity

We are looking for an AI/ML Engineer who can design and build enterprise-grade Generative AI, Agentic AI, and Machine Learning solutions while working directly with customers to solve complex business challenges. This role combines advanced technical engineering, reusable architecture design, AI evaluation and observability, and customer-facing problem-solving. You will help transform AI concepts into reliable, scalable applications that deliver measurable business value.

Systems Thinker Mode

Design reusable architectures for enterprise Generative AI, Agentic AI, and Machine Learning applications. Build measurement frameworks that work across AI use cases such as software engineering, analytics, customer support, recruiting, HR, supply chain, finance, insurance, and healthcare. Define outcome metrics covering adoption, productivity, task completion, quality, accuracy, latency, cost, user satisfaction, risk, and business value. Design data pipelines and instrumentation that capture agent traces, tool calls, token consumption, model performance, user activity, feedback signals, and workflow outcomes. Build dashboards, semantic models, evaluation workspaces, and self-service analytics that allow stakeholders to understand AI performance without depending on manual reporting. Establish standardized evaluation and observability patterns across a fragmented and rapidly changing AI technology landscape. Help shape how organizations think about AI-enabled productivity, workforce augmentation, operating models, job responsibilities, and organizational transformation. Convert successful prototypes into reusable accelerators, reference architectures, and enterprise platforms.

Builder Mode

Develop Generative AI and Machine Learning applications that solve complex enterprise and customer problems. Build intelligent agents and multi-agent workflows with planning, reasoning, tool use, memory, context management, human review, and workflow orchestration. Design production-grade Retrieval-Augmented Generation systems, including document ingestion, parsing, chunking, embedding generation, indexing, hybrid retrieval, reranking, grounding, citation generation, and hallucination mitigation. Develop secure Python services and APIs using FastAPI, Flask, or comparable frameworks. Integrate Large Language Models with enterprise databases, APIs, document repositories, SaaS platforms, knowledge bases, and business applications. Build scalable pipelines for data preparation, training, fine-tuning, inference, evaluation, deployment, monitoring, and continuous improvement. Develop Machine Learning and Deep Learning solutions across natural language processing, computer vision, forecasting, classification, recommendation, and structured-data use cases. Fine-tune and adapt models using techniques such as PEFT, LoRA, instruction tuning, prompt tuning, and domain-specific optimization when justified. Apply prompt engineering and context engineering techniques to improve accuracy, consistency, safety, and task completion. Deploy AI applications using cloud-native architectures, containers, microservices, event-driven systems, and managed AI platforms. Ensure that AI systems meet enterprise requirements for security, privacy, reliability, scalability, governance, auditability, and Responsible AI.

Client Impact Mode

Work directly with customers to understand business processes, pain points, constraints, data availability, and success criteria. Translate business requirements into AI use cases, technical architectures, delivery plans, and measurable outcomes. Facilitate discovery sessions, technical workshops, architecture reviews, demonstrations, and executive briefings. Collaborate with engineering, data, analytics, security, cloud, infrastructure, product, legal, governance, and business teams. Drive alignment across teams without relying on formal authority. Explain technical tradeoffs and model limitations honestly and constructively. Identify when conventional automation, analytics, or software engineering may be more appropriate than Generative AI. Ensure that solutions create sustainable customer value rather than short-term demonstrations.

What You NeedMinimum Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field, or equivalent practical experience.
• Three or more years of hands-on experience in Machine Learning, Artificial Intelligence, Data Engineering, Software Engineering, Generative AI, or a related area.
• Strong proficiency in Python and experience building production-quality software.
• Experience developing and deploying Machine Learning, Generative AI, or intelligent automation applications.
• Experience working with Large Language Models, AI agents, RAG systems, or modern AI development platforms.
• Strong understanding of Machine Learning fundamentals, model evaluation, data preparation, experimentation, and production deployment.
• Ability to take an AI use case from an ambiguous initial idea through architecture, implementation, evaluation, and deployment.
• Strong written and verbal communication skills, including the ability to present technical findings to leadership and nontechnical stakeholders.
• Demonstrated ownership, sound judgment, and the ability to move quickly without compromising critical quality, security, or governance requirements.

Technical CapabilitiesGenerative AI and Agentic Systems:

Hands-on experience with one or more agentic or Generative AI frameworks, such as:

• LangGraph
• LangChain
• Google Agent Development Kit
• Anthropic Agent SDK
• OpenAI SDK
• AWS Strands Agents
• Microsoft Semantic Kernel
• CrewAI
• AutoGen

Experience should include several of the following:

• Agent orchestration
• Tool and API integration
• Planning and task decomposition
• State and workflow management
• Short-term and long-term memory
• Prompt and context engineering
• Model routing and fallback strategies
• Human-in-the-loop workflows
• Agent evaluation and observability
• Multi-agent communication
• Model Context Protocol
• Agent-to-Agent communication patterns

Retrieval-Augmented Generation

Experience designing and implementing RAG solutions involving:

• Enterprise document ingestion
• Document parsing and metadata extraction
• Chunking and indexing strategies
• Embedding models
• Vector and keyword search
• Hybrid retrieval
• Query transformation and decomposition
• Reranking
• Grounded response generation
• Citation and source attribution
• Retrieval and generation evaluation
• Hallucination mitigation
• Access-controlled retrieval

Experience with one or more vector-search platforms such as FAISS, Pinecone, Weaviate, ChromaDB, OpenSearch, Elasticsearch, PostgreSQL with pgvector, or comparable technologies is desirable.

Machine Learning and Deep Learning

Strong understanding of:

• Supervised and unsupervised learning
• Neural networks and transformer architectures
• Natural language processing
• Computer vision
• Statistical modeling
• Feature engineering
• Model training and validation
• Performance optimization
• Experiment design
• Error analysis
• Model monitoring and drift detection

Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face Transformers.

Backend and Data Engineering

• Strong Python programming skills.
• Experience developing REST APIs and backend services using FastAPI, Flask, or similar frameworks.
• Familiarity with microservices, event-driven architectures, asynchronous processing, and clean architecture principles.
• Experience with SQL and enterprise data platforms such as PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, Databricks, or similar systems.
• Ability to design data pipelines for model inputs, agent telemetry, evaluation results, user feedback, and business outcome metrics.

Cloud and AI Platforms

Experience with at least one major cloud ecosystem:

• AWS, including Bedrock, AgentCore, SageMaker, Lambda, ECS, EKS, or related services.
• Google Cloud, including Vertex AI, Gemini Enterprise, BigQuery, Cloud Run, GKE, or related services.
• Microsoft Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Functions, AKS, or related services.

Knowledge of cloud security, identity management, networking, private connectivity, monitoring, scalability, and cost optimization is highly desirable.

DevOps, MLOps, and LLMOps

• Experience with CI/CD pipelines, automated testing, containerization, and deployment automation.
• Familiarity with model deployment, versioning, monitoring, and performance evaluation.
• Understanding of LLM observability, tracing, latency, and cost optimization.
• Ability to maintain reliable AI systems through continuous monitoring and improvement.

CKCODECONNECT is an Equal Opportunity Employer and does not discriminate based on race, age, color, religion, sex, sexual orientation, gender identity, national origin, veteran, disability status or any other characteristic protected by applicable law.