Expert AI Engineer

Ciklum

As an Expert AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

Lead the design, development and deployment of advanced AI systems across Data Science and AI Engineering domains
Architect and implement scalable AI pipelines and LLM-driven applications, including retrieval-augmented generation (RAG), orchestration and multi-agent systems
Serve as the technical authority during client engagements, ensuring architecture quality, scalability, performance and reliability
Contribute hands-on to development activities, from experimentation and prototyping to production-grade implementation
Collaborate with cross-functional engineering, data, and product teams to align technical solutions with client and business objectives
Apply MLOps and LLMOps best practices for CI/CD, observability, evaluation and continuous improvement of AI models and pipelines
Integrate AI services with enterprise platforms (e.g., Confluence, Jira, GitHub, CRM, ERP) and ensure seamless interoperability
Drive innovation through internal frameworks and accelerators
Ensure all AI solutions comply with security, data privacy and responsible-AI standards
Mentor engineering peers and support knowledge sharing across teams and practices (e.g., contributing to Ciklum’s AI Academy)

Act as technical SME in presales conversations, translating business challenges into viable AI engineering solutions
Partner with delivery and account teams to shape long-term client engagement strategies based on engineering excellence

Support recruitment efforts through technical assessments, interviews, and candidate evaluation
Mentor team members and contribute to cross-skilling initiatives within the AI and Engineering service lines
Uphold engineering excellence by promoting best practices, code quality, and delivery standards across projects

We know that sometimes, you can’t tick every box. We would still love to hear from you if you think you’re a good fit!

8+ years of professional experience in software, data, or AI engineering, including at least 3–4 years of hands-on experience designing and implementing AI/ML solutions
BSc, MSc, or PhD in Computer Science, Mathematics, Engineering, or a related quantitative field
Deep understanding of probability, statistics, and the mathematical foundations of machine learning and optimization
Proven experience building and deploying advanced AI systems, including Large Language Models (LLMs), multimodal, and generative AI architectures
Exposure to agentic system design, retrieval-augmented generation (RAG) and prompt engineering techniques
Strong proficiency in Python and common AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent)
Solid understanding of modern AI engineering practices, including model lifecycle management, observability, evaluation, versioning and continuous improvement
Familiarity with AI solution delivery methodologies (e.g., CRISP-ML(Q), TDSP or modern agile ML lifecycles)
Ability to visualize, interpret, and communicate model outputs and insights effectively using modern tools and dashboards

Proven experience in architecting and implementing end-to-end AI/ML solutions — from data ingestion and model training to deployment, monitoring and optimization
Strong software engineering skills for AI system development, including data processing, API integration, and model serving (Python, SQL and optionally Java/Scala or similar)
Hands-on experience with cloud-native AI platforms and services (AWS SageMaker, Azure ML, GCP Vertex AI or NVIDIA AI stack)
Proficiency in designing scalable ML/LLM pipelines and applying MLOps/LLMOps best practices (CI/CD, orchestration, monitoring, versioning, and deployment automation)
Experience with diverse data modalities (structured, text, image, audio, video) and multimodal model integration
Familiarity with handling complex data scenarios such as class imbalance, time-series forecasting and anomaly detection
Understanding of security, data governance and compliance considerations in AI system design

Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom or Technology sectors
Proven ability to translate business and operational requirements into robust AI system architectures that deliver measurable impact
Familiarity with challenges of deploying AI in regulated environments and ensuring compliance with data privacy and protection frameworks (e.g., GDPR, CCPA, PCI DSS)
Experience managing sensitive or high-value data (PII, PHI), implementing strong security, governance and access control mechanisms
Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management or workflow systems)

Proven experience delivering production-grade AI solutions that achieve measurable business and operational outcomes
Strong ownership of the full AI engineering lifecycle — from problem framing and architecture design to deployment, optimization, and continuous improvement
Ability to align technical decisions with business priorities, ensuring scalability, reliability, and measurable value from AI initiatives
Excellent collaboration and communication skills to work effectively with cross-functional stakeholders, delivery teams, and clients
High degree of autonomy, accountability, and attention to detail in managing complex, multi-component AI systems

Strong background in software or solution architecture, ideally with previous experience as a Software or Data Architect
Proven ability to design scalable, distributed, and fault-tolerant AI architectures, integrating APIs, microservices, and event-driven components
Experience with MLOps and LLMOps practices, including pipeline automation, containerization (Docker, Kubernetes), and continuous deployment of AI models
Deep learning expertise using TensorFlow, PyTorch, or JAX, including fine-tuning and optimization of large models
Hands-on experience with Large Language Models (LLMs), Generative AI applications, and agentic or RAG-based systems
Advanced SQL and familiarity with modern data platforms (Databricks, Snowflake, or equivalent)
Experience with Big Data and streaming frameworks (Apache Spark, Kafka, Flink, etc.)
Understanding of NoSQL and graph databases (e.g., Cassandra, Neo4j) and their role in AI knowledge management
Experience with cloud-native architectures and certified expertise in AWS, Azure, or GCP AI/ML services
Exposure to research or innovation projects, with publications or open-source contributions considered an advantage