Senior AI Engineer / Agentic AI Architect

Description

About the Role

Requirements

• Required Qualifications & Skills
• Experience
• 8–12+ years of experience in Software Engineering.
• 5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.
• AI / Machine Learning
• Strong understanding of:
• Machine Learning
• Deep Learning
• NLP
• Transformer architectures
• Large Language Models (LLMs)
• Embedding models
• Software Engineering
• Expert-level Python programming.
• Strong software engineering fundamentals.
• Experience building production-grade backend systems.
• RESTful API and microservices development.
• Async programming and scalable architectures.
• Experience with FastAPI, Flask, or similar frameworks.
• Agentic AI
• Hands-on experience designing and implementing Agentic AI solutions using one or more of:
• LangGraph
• CrewAI
• OpenAI Agents SDK
• AutoGen
• Semantic Kernel
• LlamaIndex Workflows
• Experience in:
• Multi-agent orchestration
• Planning agents
• Tool calling
• Human-in-the-loop workflows
• Memory management
• State management
• Agent collaboration patterns
• Retrieval-Augmented Generation (RAG)
• Strong experience building enterprise RAG platforms.
• Embedding models, such as:
• OpenAI
• Voyage AI
• BGE
• E5
• Instructor
• Cohere
• Vector databases, such as:
• Pinecone
• Qdrant
• Milvus
• Weaviate
• ChromaDB
• Graph databases, such as:
• Neo4j
• Amazon Neptune
• Memgraph
• Search technologies, including:
• Hybrid Search
• BM25
• Dense Retrieval
• Sparse Retrieval
• Semantic Search
• Metadata Filtering
• Re-ranking
• Knowledge Graph integration
• LLM Evaluation
• Experience designing systematic evaluation frameworks using tools such as:
• Ragas
• TruLens
• DeepEval
• OpenAI Evals
• LangSmith Evaluation
• Understanding of:
• Hallucination detection
• Faithfulness
• Answer relevancy
• Context precision
• Context recall
• Groundedness
• Toxicity
• Regression testing
• Guardrails & Observability
• Guardrails:
• Guardrails AI
• NeMo Guardrails
• OpenAI Moderation
• Prompt Injection Detection
• PII masking
• Content filtering
• Observability:
• LangSmith
• Langfuse
• Arize Phoenix
• Weights & Biases
• MLflow
• Experience with:
• Prompt tracing
• Token analytics
• Cost monitoring
• Latency monitoring
• User feedback loops
• Production debugging
• Prompt Engineering
• Expertise in:
• Chain-of-Thought (CoT)
• ReAct
• Tree of Thoughts
• Self-Consistency
• Few-shot prompting
• Structured prompting
• Function Calling
• JSON mode
• Prompt optimization
• Prompt caching
• Context window optimization
• Token usage optimization
• Cost optimization
• Model Deployment & Inference
• Hands-on experience deploying open-source LLMs.
• Preferred models:
• Llama
• Mistral
• Qwen
• Gemma
• DeepSeek
• Inference engines:
• vLLM
• TensorRT-LLM
• Ollama
• TGI (Text Generation Inference)
• SGLang
• GPU optimization
• Batch inference
• Model serving
• Autoscaling
• Multi-GPU deployment
• Quantization (GGUF, GPTQ, AWQ, FP8, INT8, INT4)
• MLOps / AI Platform
• Kubeflow
• Docker
• Kubernetes
• GitHub Actions / GitLab CI
• Model versioning
• Experiment tracking
• Feature stores
• Continuous evaluation
• Continuous deployment
• Cloud Platforms
• Experience with one or more:
• Google Cloud Platform (Vertex AI)
• Microsoft Azure AI
• AWS Bedrock
• OpenAI Azure
• Database Technologies
• PostgreSQL
• Oracle
• MongoDB
• Redis
• Elasticsearch / OpenSearch
• Soft Skills
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management.
• Ability to lead technical discussions and architecture reviews.
• Experience mentoring engineering teams.
• Ability to work in Agile delivery environments.
• Banking & Industry Experience (Nice to Have)
• Banking or Financial Services domain experience.
• Experience with enterprise AI governance and Responsible AI frameworks.
• Knowledge of SAMA, NCA, or other financial regulatory environments.
• Experience building AI copilots and enterprise AI assistants.
• Knowledge of OCR, document intelligence, and intelligent automation.
• Experience integrating AI solutions with BPM/workflow platforms such as Appian, Camunda, or Pega.
• Education & Certifications
• Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field.
• The following professional certifications are an advantage:
• Google Professional Machine Learning Engineer
• Microsoft Azure AI Engineer Associate
• AWS Certified Machine Learning – Specialty
• Databricks Machine Learning Professional
• NVIDIA AI Certifications
• OpenAI or Anthropic ecosystem certifications (where applicable)
• Key Competencies
• Agentic AI | Multi-Agent Systems | Large Language Models (LLMs) | RAG | Vector & Graph Databases | LLM Evaluation | Guardrails & Observability | Prompt Engineering | LLM Deployment & Inference | MLOps | Python | APIs & Microservices | Cloud AI Platforms | Responsible AI

Benefits

• What We Offer
• Competitive compensation based on experience and qualifications.
• Opportunity to work on enterprise-scale technology and digital transformation projects.
• Exposure to banking, financial services, AI, data, and emerging technology environments.
• Professional growth and learning opportunities.
• Collaborative and technically driven work environment.
• Opportunity to work with experienced technology and consulting professionals.
• Disclaimer
• This job description is intended to convey information essential to understanding the scope of the role and is not exhaustive of all responsibilities, skills, or qualifications required. CodeNinja reserves the right to modify duties and responsibilities at any time.