AI Engineer
Full Time
13 Oct 2025
Verified by Turrior
Content + Source + Freshness • 14 Feb 2026 • 95% confidence
76 / 100
Offer value
Moderate value with a focus on skill requisites for niche AI technologies, fostering potential growth in a dynamic environment.
- Engagement with cutting-edge AI technologies
- Opportunities for skill development and growth
- Collaborative environment among technology specialists
Pros
- Engagement with advanced AI frameworks and tools
- Room for growth in an innovative technology space
- Collaborative role within focused teams
Cons
- Lack of specified salary information may deter candidates
- Potentially high expectations in building AI solutions
- Specific expertise required may limit applicant pool
Who it's for
Mid-level • On-site
Good fit
- Experienced AI engineers eager to develop applications
- Candidates skilled in working with advanced frameworks
- Individuals ready to contribute to innovative projects
Not recommended for
- Job seekers without a strong technical background in AI
- Those preferring remote work arrangements
- Individuals needing clear-cut job roles
Motivation fit
Desire to work with advanced AI technologiesInterest in developing real-world applicationsEagerness to collaborate with research and product teams
Key skills
Deep understanding of AI frameworksCollaboration and teamwork abilitiesTechnical proficiency in programming and AI deployment
Score: 76/100 AI verified analysis
About the job
Job Title : AI EngineerCompany : Space O TechnologiesYears of Experience: 4+ yearsLocation: AhemdabadRole Type: Full-TimeSalary: Not specifiedEligibility: Candidates with deep expertise in AI agent systems, RAG pipelines, LLM tooling, and production-grade GenAI deploymentsRole Overview:Design, develop, and deploy intelligent AI agent systems and retrieval-augmented generation (RAG) pipelines. Build production-grade GenAI applications using frameworks like LangGraph, CrewAI, and LangChain, integrating agents with internal tools, APIs, and databases. Collaborate with research and product teams to optimize multi-agent workflows and ensure robust monitoring, logging, and observability in production environments.Key Responsibilities:Design and implement AI agent frameworks with memory, tool use, task decomposition, and multi-turn conversation capabilities.Build and optimize RAG pipelines using LangChain, LlamaIndex, or custom vector search architectures.Integrate agents with internal tools, APIs, and databases to support real-world applications.Collaborate with ML researchers and product teams to experiment with novel architectures and orchestrators like LangGraph and CrewAI.Monitor, evaluate, and optimize model performance using telemetry, logging, and analytics.Deploy production-ready systems with robust CI/CD pipelines, testing, and monitoring.Stay current with advances in LLMs, agentic frameworks, and vector search infrastructure.Skills and Qualifications:Programming: Python (advanced), TypeScript/Node.js (nice to have)AI Frameworks: LangGraph, CrewAI, LangChain, LlamaIndex, OpenAI, Hugging FaceAgent Systems: Designing multi-agent workflows, task planning, memory handling, inter-agent communicationRAG Architecture: Document loaders, chunking strategies, embeddings, hybrid search, contextual rerankingLLM Tooling: OpenAI GPT-4/4o, Claude, Gemini, local models (Mistral, LLaMA)Infrastructure: Vector DBs (Weaviate, Pinecone, Qdrant, Elasticsearch), Postgres, MongoDBMLOps: Prompt engineering, model evaluation, A/B testing, observabilityDeployment: REST APIs, FastAPI, Docker, CI/CD pipelinesAdditional: Strong communication skills, independent initiative, familiarity with GenAI safety, audit logging, and access controls
