About Us:
Systango Technologies Limited (NSE: SYSTANGO) is a digital engineering company that offers enterprise-class IT and product engineering services to different size organizations. At Systango, we have a culture of efficiency - we use the best-in-breed technologies to commit quality at speed and world-class support to address critical business challenges. We leverage Gen AI, AI/Machine Learning and Blockchain to unlock the next stage of digitalization for traditional businesses. Our handpicked team is adept at web & enterprise development, mobile apps, QA and DevOps. Ulster University, Sila, Cuentas, Youtility, Porsche, MGM Grand, Deloitte, Grindr, and Tawk.to are some of the top clients that have entrusted us to enhance their digital capabilities and build disruptive innovations. We believe in making the impossible, Possible and we do it literally.
Role Overview
We are seeking an AI Engineer to design, build, and deploy production-grade AI systems using Generative AI and Large Language Models (LLMs). This role emphasizes applied GenAI engineering, including Retrieval-Augmented Generation (RAG), AI workflow orchestration, and multi-agent systems, rather than deep ML theory or academic research.
You will work closely with product, platform, and engineering teams to integrate AI capabilities into real-world applications and business workflows.
Key Responsibilities
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Build and maintain AI-enabled services and applications using Python.
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Design and implement RAG pipelines, including document ingestion, embeddings, retrieval, and response synthesis.
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Integrate LLMs via APIs (e.g., OpenAI, Azure OpenAI, Anthropic) into production systems.
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Develop AI workflows that combine retrieval, prompts, tools, agents, and post-processing.
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Build and manage multi-agent systems, defining agent roles, coordination logic, and shared context.
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Use AI orchestration frameworks such as LangGraph, Microsoft Semantic Kernel, CrewAI, or similar tools.
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Perform LLM fine-tuning or model customization to improve task-specific performance.
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Evaluate and monitor AI outputs for quality, accuracy, latency, cost, and reliability.
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Collaborate with cross-functional teams to translate business needs into scalable AI solutions.
Required Skills & Experience
Programming & Software Engineering
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Strong proficiency in Python for AI application development.
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Solid understanding of software engineering best practices, including modular design, testing, and maintainability.
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Proficient with Git and GitHub for version control and collaboration.
Generative AI, RAG & LLMs
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Hands-on experience using LLMs via APIs.
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Strong experience with Retrieval-Augmented Generation (RAG), including:
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Embeddings creation and management
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Vector databases and similarity search
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Prompt + retrieval integration
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Experience with prompt engineering, inference, and response evaluation.
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Practical experience with LLM fine-tuning or customization (e.g., instruction tuning, domain adaptation).
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Understanding of LLM limitations, hallucinations, latency, and cost trade-offs.
AI Workflow Orchestration & Agent Systems
Experience designing multi-step AI workflows (sequential, branching, parallel, human-in-the-loop).
Hands-on experience with AI orchestration and agent frameworks such as:
LangGraph
Microsoft Semantic Kernel
CrewAI
Or equivalent tools
Ability to design and operate multi-agent architectures, including shared memory, tool usage, and coordination.
Understanding of guardrails, retries, fallbacks, and failure handling in AI pipelines.
Data, APIs & Storage
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Experience building and consuming RESTful APIs using FastAPI, Flask, or Django.
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Comfortable working with structured and unstructured data (JSON, CSV, text, documents).
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Experience with SQL or NoSQL databases, including vector databases for RAG use cases.
Deployment & Production Readiness
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Experience deploying AI-enabled services to production environments.
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Understanding of scalability, security, observability, and monitoring for AI systems.
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Familiarity with cloud platforms and containerization (Docker; Kubernetes is a plus).
