Solution Architect Industrial Agents
- Cognite
- 10 - 12 years
- Bengaluru
- 1 year ago
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Job Description
- As a Solutions Architect, you will be an expert in how we implement our Cognite SaaS offerings and applications and ensuring that the data is efficiently modeled and contextualized in Cognite Data Fusion (E.g graph and relational)
- Integrations are well thought out and robust Important quality criteria for the solution are met (E.g. CI/CD, logging, security)
- Operational responsibility and support agreements for the delivered solution are clearly defined
- Design overall technical solution and ensure technical fit within the customer ecosystem and target architecture
- Design integration and data model using Cognite data connectors, Cognite platform components, SQL, Python/Java and Rest APIs
- Advisor to our customers and partners on Cognite Data Fusion, Cognite Atlas AI and Cognite products, solutions, and platforms
- Design, develop, and implement generative AI solutions with a strong focus on AI agents, multi-agent systems, and the latest generative AI technologies to drive business innovation and enhance customer experiences.
- Collaborate with cross-functional teams to understand business requirements and translate them into technical specifications for generative AI solutions.
- Architect scalable AI solutions, including AI agents, that integrate seamlessly with existing systems and leverage cutting-edge technologies.
- Drive development of AI frameworks, and tools, with a particular emphasis on generative AI, AI agents, natural language processing (NLP), agent orchestration, and reinforcement learning.
- Provide technical leadership and mentorship to software engineers, with a focus on developing skills in generative AI, AI agents, and advanced AI technologies.
- Stay ahead of the latest trends and advancements in AI, particularly in generative AI, AI agents, and associated technologies, to ensure our solutions remain cutting-edge and industry-leading.
- Develop and deploy AI agents capable of autonomous task execution, environment adaptation, and effective interaction with users and systems, utilizing the latest generative AI frameworks and models.
- Collaborate with Global Delivery on customer engagements, to demonstrate the value and feasibility of AI-driven solutions, particularly those involving AI agents, generative AI technologies, and advanced AI methodologies.
- Vector Database Proficiency: Knowledge of vector databases like Pinecone, Milvus, Weaviate, or Faiss, including their architecture and use cases.
- Vector Embedding Creation: Experience in generating vector embeddings from textual, visual, or other data using common industry models.
- Skills in creating, managing, and optimizing indexes for efficient similarity search within vector databases, including knowledge of ANN search algorithms.
- Data Ingestion and Querying: Proficiency in ingesting large datasets into vector databases and writing optimized queries for complex similarity searches.
- Scaling and Performance Tuning: Ability to scale vector databases to handle large datasets and optimize search performance through resource management and index tuning.
- Document Retrieval and Prompt Engineering: Skills in designing effective document retrieval strategies and crafting prompts that leverage retrieved documents in the generation process.
- Data Pipeline and Deployment: Expertise in managing data pipelines for RAG systems, from ingestion to retrieval and generation, and deploying RAG systems at scale.
- Cross-Skills in Embedding and Security: Proficiency in generating and transforming embeddings for both retrieval and generation tasks, with a focus on security, compliance, and API integration
We believe most of these should match your experience
- 10+ years of experience in software engineering, with a focus of at least 5+ years in AI and 2+ years on Generative AI, machine learning, or intelligent systems.
- Proven experience in developing and deploying multi-agent systems, preferably using frameworks like LangChain. (Mandatory experience)
- Experience with knowledge graphs, graph databases, or related technologies (e.g., Neo4j, RDF, SPARQL).
- RAG Architecture Understanding: In-depth knowledge of Retrieval-Augmented Generation (RAG) systems, integrating retrieval with generative models to produce informed responses.
- Model Integration and Fine-Tuning: Experience in integrating and fine-tuning pre-trained models with retrieval systems in RAG pipelines for enhanced performance
- Proficiency in Python, JavaScript, or other relevant programming languages.
- Deep understanding of multi-agent frameworks, including agent communication, decision-making, and learning strategies.
- Strong background in knowledge of graph technologies and their applications.
- Experience in Time Series Analysis involving detecting trends and patterns, forecasting future values using models like ARIMA, SARIMA, Prophet, and LSTMs, and identifying anomalies or outliers in time series data.
- Experience in optimizing the performance of AI agents in real-time, high-load environments, including the use of reinforcement learning and other adaptive techniques.
- Familiarity with cloud platforms (e.g., AWS, Azure) and containerization technologies (e.g., Docker, Kubernetes).
- Experience with API development and integration.
- Strong problem-solving skills and the ability to think critically about complex systems.
- Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
- Ability to work in a fast-paced, collaborative environment and manage multiple priorities.
- Experience in the industrial sector or with industrial data. (Not mandatory)
- Knowledge of big data technologies (e.g., Hadoop, Spark) and real-time processing frameworks.
- Data Handling and Storage: Proficiency in reading and writing data in various formats (CSV, JSON, SQL) and using storage tools like SQLite and SQL databases.
- Feature Engineering and Dimensionality Reduction: Expertise in creating new features to enhance model performance and using techniques like PCA and t-SNE to reduce feature dimensionality while retaining key information.
- Expertise with Model Interpretability by using Tools and frameworks like SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and feature importance to understand model predictions.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Solution ArchitectEmployement Type: Full timeContact Details:
Company: CogniteLocation(s): Bengaluru+ View Contact
Keyskills: Performance tuning Architecture Development Manager SQLite Machine learning Javascript JSON Resource management SQL Python
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