AINE AI
Website:
aine.ai
Job details:
About the RoleWe are seeking a highly experienced and commercially minded AI Engineering Manager to lead our artificial intelligence and software engineering initiatives. In this role, you will bridge the gap between cutting-edge AI research and robust, scalable software delivery.
You will lead a cross-functional team of AI engineers, software developers, and data specialists to architect, build, and deploy high-quality AI-driven solutions. Because we prioritize exceptional client experiences, a significant portion of this role involves direct client communication—translating complex technical AI concepts into clear business value. You will champion high-quality delivery, robust security, and site reliability standards across all our products.
Key Responsibilities- Technical Leadership & Architecture: Define the technical vision and architecture for AI-powered applications, ensuring they are scalable, secure, and seamlessly integrated with traditional software backends, cloud infrastructure, and databases.
- Team Management & Mentorship: Build, lead, and mentor a high-performing team of software and AI engineers. Foster a culture of technical excellence, continuous learning, and collaboration.
- Client Engagement & Communication: Act as the primary technical point of contact for key clients. Present technical strategies, manage expectations, gather requirements, and confidently communicate project progress and ROI.
- Delivery & Quality Assurance: Oversee the end-to-end software development lifecycle. Implement rigorous testing, code review, and CI/CD practices to ensure flawless, high-quality deployments.
- Operational Excellence: Champion Site Reliability Engineering (SRE) and Security best practices to ensure our AI systems are highly available, performant, and secure by design.
Required Qualifications (Must-Haves)- Experience: 10+ years of progressive experience in software engineering, data engineering, or machine learning, with at least 3+ years in a direct people management or technical leadership role.
- AI & Machine Learning Expertise: Deep understanding of AI/ML concepts, including Large Language Models (LLMs), generative AI, deep learning, and traditional machine learning pipelines.
- Software Engineering Foundation: Strong background in building scalable, production-grade enterprise software.
- Cloud & Infrastructure: Hands-on expertise designing and deploying solutions in major cloud environments (AWS, GCP, or Azure).
- Database Mastery: Deep knowledge of data modeling and architecture across relational, NoSQL, and vector databases.
- Client Communication: Exceptional verbal and written communication skills. Proven ability to face clients, lead executive-level technical discussions, and translate complex concepts to non-technical stakeholders.
Technical Stack & EnvironmentWe know great engineers can learn new tools. You should be highly proficient in a few of these areas, but many are considered optional/nice-to-have.
1. AI & Data Ecosystem (Core)
- Languages: Python (Required), Go, Java, or C++.
- AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn.
- LLM & GenAI Stack: LangChain, LlamaIndex, Hugging Face, OpenAI APIs.
- MLOps (Optional): MLflow, Kubeflow, Weights & Biases.
2. Software Engineering & Backend
- Frameworks: FastAPI, Flask, Django, Node.js, or Spring Boot.
- Architecture: Microservices, Event-Driven Architecture, RESTful APIs, GraphQL.
3. Data & Databases
- Relational: PostgreSQL, MySQL.
- NoSQL (Optional): MongoDB, Redis, Cassandra.
- Vector Databases (Highly Preferred): Pinecone, Milvus, Weaviate, or pgvector.
- Data Processing (Optional): Apache Spark, Kafka, Airflow.
4. Cloud, SRE & DevOps
- Cloud Platforms: AWS (EC2, SageMaker, S3), GCP (Vertex AI, GKE), or Microsoft Azure.
- Containerization: Docker, Kubernetes (K8s).
- IaC (Optional): Terraform, CloudFormation, Ansible.
- CI/CD: GitHub Actions, GitLab CI, Jenkins.
5. SRE & Security (Crucial but specific tooling is optional)
- Observability & Monitoring: Datadog, Prometheus, Grafana, ELK stack. Ensure systems are observable and downtime is minimized.
- Security & DevSecOps: Familiarity with OWASP, data encryption, IAM, and compliance standards (e.g., SOC2, HIPAA, GDPR). Experience securing AI models against prompt injection and data leakage.
What Success Looks Like in Year One- Successfully delivered robust AI solutions into production that meet strict SLA and quality thresholds.
- Established a strong, trusting relationship with key clients through transparent and effective technical communication.
- Implemented standardized MLOps, SRE, and DevSecOps protocols across your team's projects.
- Fostered a highly motivated, high-retention engineering team.
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