About the Role
In this role, you will deliver production-grade AI applications with a focus on scalability, security, observability, governance, and performance. You will work across Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.
The ideal candidate will have strong software engineering fundamentals and deep expertise in LLMs, RAG, Agentic AI, and modern AI engineering practices. They will have 8–12+ years in software engineering, including 5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.
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Key Responsibilities
- Design and develop enterprise-grade AI solutions using modern LLMs and Agentic AI frameworks.
- Architect multi-agent systems capable of planning, reasoning, tool usage, and workflow orchestration.
- Build production-ready RAG platforms integrating structured and unstructured enterprise data.
- Design scalable APIs and AI services using Python and modern backend frameworks.
- Implement robust evaluation frameworks for LLM quality, safety, and performance.
- Optimize AI applications for latency, throughput, and infrastructure cost.
- Deploy and manage open-source LLMs in production environments.
- Collaborate with architects, product owners, business analysts, and DevOps teams to deliver enterprise AI platforms.
- Ensure compliance with enterprise security, governance, and responsible AI practices.
- Mentor engineering teams and contribute to AI best practices and reusable frameworks.
Expected Deliverables
The selected candidate should be capable of independently designing and delivering:
- Enterprise AI platforms
- Multi-agent AI systems
- RAG-based knowledge assistants
- AI copilots
- LLM evaluation frameworks
- Production-ready AI APIs
- AI observability and monitoring solutions
- Secure, scalable, and cost-optimized AI deployments suitable for enterprise production environments.
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
- Experience with:
- GPU optimization
- Batch inference
- Model serving
- Autoscaling
- Multi-GPU deployment
- Quantization (GGUF, GPTQ, AWQ, FP8, INT8, INT4)
MLOps / AI Platform
- Experience with:
- MLflow
- 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
- Experience with:
- 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.
