1. End-to-End Model Development

• Design, implement, and deploy distributed, high-volume, high-performance, low-latency machine learningsolutions, with a focus onGenAI models, and especiallyLLM integrations and API-driven architectures
• Take ownership of your models throughout their entire lifecycle:

• Data exploration and cleaning to build reproducible, versioned datasets
• State-of-the-artresearch toidentifythe best architectures for the problem (e.g., transformers, RAG, fine-tuning)
• Implementation, training, and optimization in reproducible environments
• Deployment, monitoring, and maintenance in production
• Optimizemodels for performance, latency, and cost efficiency, especially in LLM serving and inference

2. Software Engineering for AI

• Write clean, modular, and well-documented code in Python (FastAPI,Pydantic,asyncio)
• Apply best practices in:
  • Testing (unit, integration, end-to-end)
  • CI/CD (GitHub Actions, GitLab CI,ArgoCD)
  • Observability (logging, monitoring, tracing)
• Ensure security and compliance (data protection, access controls, encryption)
• Integrate models and code into CI/CD pipelines for seamless deployment

3. AI & ML Integration & API Development

• Design and implementAI-powered solutions that integrate with APIs, microservices, and event-driven architectures
• Develop andoptimizeAIpipelines for:
  • Dataset cleaning,preprocessingand model training
  • Fine-tuning (domain adaptation, instruction tuning)
  • Retrieval-Augmented Generation (RAG) (vector databases, semantic search)
  • Prompt engineering (optimizinginputs for performance, cost, and accuracy)

• Model evaluation (benchmarking, bias detection, drift analysis)
• Build scalable, secure, and cost-efficient serving infrastructure (e.g.,FastAPI,vLLM)
• Debug andoptimizeperformance (latency, throughput, token efficiencyfor Transformer based architectures)

4. Enterprise AI & MLOps

• Deploy andmonitorAI models in production
• Design and implementMLOpspipelines for:
  • Model training, fine-tuning, and evaluation
  • Model versioning and lineage tracking
  • A/B testing and canary deployments
• Ensure scalability and reliability (auto-scaling, fault tolerance, disaster recovery)
• Collaborate with data engineers to build data pipelines (batch, streaming, real-time)

5. Collaboration & Technical Leadership

• Work closely with product owners, DevOps, and quality assurance in an agile, cross-functional team
• Mentor junior engineers and promote best practices in AI/ML and software engineering
• Translate product requirements into technical solutions and architectural decisions
• Document architectures, decisions, and best practices for internal and client-facing use
• Develop relationships with internal and external stakeholders, including clients and partners

6. Innovation & Continuous Improvement

• Stay ahead of the latestAI and MLarchitectures (transformers, Mixture of Experts, sparse attention).
• Experiment withcutting-edgetechniques (quantization, distillation, speculativedecoding).

• Evaluate and benchmark open-source and proprietary models (Llama, Mistral,Mixtral, GPT-4, Claude).
• Bring your own ideas through vector8’s ideation process.
• Contribute to vector8’s AI accelerators (reusable components for common industry problems).
• Embrace a strategic and continuous improvement mentality to drive innovation.

• 5+ years of experience in AI/ML engineering, software development, or a related field
• Expertise in LLM architectures and training methodologies:
  • Transformers, attention mechanisms, fine-tuning, RAG, quantization
  • Prompt engineering, model evaluation, bias detection
• Strong knowledge of machine learning architectures: fully connected, CNN, LSTM, transformers and classical ML models
• Strong software engineering skills:
  • Proficient in Python (FastAPI,Pydantic,asyncio, type hints)
  • Experience with API development
  • Familiarity with modern tool chains (Docker, Kubernetes, Terraform)
• Hands-on experience with LLM integrations:
  • LLM providers
  • Vector databases (Pinecone, Weaviate, Milvus)
  • Model serving (vLLM, TGI, KServe)
• Experience with MLOpsand production deployments

• Understanding of enterprise challenges:
  • Security, compliance, scalability, costoptimization.
• Experience with relational and non-relational databases.
• Strong problem-solving and debugging skills.
• Excellent communication and collaboration skills (fluent in English; German is a strong plus).
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related field.
• Experience with multi-cloud environments (AWS, Azure, GCP).
• Experience with code optimization (e.g., model quantization, parallelization).

• A great compensation package with competitive benefits, including:
• Flexible working hours, including remote work options (hybrid model).
• 25daysof paid vacation per year, plusadditionalflex days.
• Private health and life insurance&pension plan for long-term security.
• Home office allowance &Lunch vouchers to enjoy meals on us.
• Discounted fitness memberships to stay active.
• 50% reimbursement of public transport costs.
• Free coffee, fruit, and snacks to keep you fueled.
• Access to the latest technologies(LangDock, Claude Code for developers)
• Grants for training, coaching, and conferences to support your continuous learning.

• Opportunities to attend industry events and representvector8as a thought leader.
• A less-formal work environment where authenticity and collaboration thrive.
• A diverse and inclusive team that values curiosity, ownership, and innovation.