Accolite

Director – Technical Solutions & Engineering / Solutions Architect

Job Description

Role Overview

The Technical Solutions & Engineering Director is a hands-on technology leader responsible for architecting and delivering enterprise-grade, cloud-native, and AI-enabled solutions. The role demands strong technical depth in Java, Microservices, React, and Kafka, combined with the ability to conceptualize and integrate AI-driven capabilities into modern digital platforms.
This leader will bridge business goals and technology implementation — driving innovation, pre-sales support, and delivery excellence.

Key Responsibilities

1. Solution Architecture & Design

  • Architect scalable, secure, and high-performance enterprise applications using Java (Spring Boot) and Microservices architecture.
  • Design event-driven and streaming architectures leveraging Apache Kafka for real-time processing and data pipelines.
  • Lead frontend architecture using React and modern UI frameworks for responsive, high-performing web applications.
  • Integrate AI and machine learning components into enterprise solutions — such as personalization, predictive analytics, chatbots, and process automation.
  • Define architecture patterns, technical roadmaps, and modernization strategies aligned with client business goals.

2. Pre-Sales & Solutioning

  • Collaborate with business and sales teams to craft winning proposals, RFP responses, and solution blueprints.
  • Conceptualize and demonstrate AI-powered solutions using ML models, NLP, and cloud AI services (AWS SageMaker, Azure AI, Vertex AI, OpenAI APIs, etc.).
  • Lead technical discovery, estimation, and architecture definition workshops with clients.
  • Prepare and deliver high-impact technical presentations, whitepapers, and PoCs.

3. Implementation & Delivery Leadership

  • Provide hands-on technical leadership during the entire delivery lifecycle.
  • Guide engineering teams on best practices in Java, Microservices, React, and Kafka ecosystems.
  • Ensure architectural consistency, scalability, and quality during implementation.
  • Drive DevOps maturity — containerization (Docker, Kubernetes), CI/CD automation, and observability setup.
  • Oversee AI solution implementation — integrating APIs, data pipelines, and MLOps workflows.

4. Innovation & Technology Leadership

  • Identify opportunities to embed AI/ML, data analytics, and automation in client solutions.
  • Lead the creation of accelerators, reusable components, and frameworks across Cloud, Microservices, and AI ecosystems.
  • Stay abreast of emerging tech (Generative AI, LLMs, edge computing, serverless architecture) and assess their business value.
  • Drive internal R&D and proof-of-concepts showcasing innovative technical capabilities.

5. People & Stakeholder Leadership

  • Build and mentor a strong architecture and engineering team.
  • Partner with delivery, data, and AI teams for end-to-end solution ownership.
  • Establish architecture governance, knowledge sharing, and technical excellence practices.
  • Act as a trusted advisor to CXOs and client technology leaders.

Key Technical Skills

Core Technologies

  • Backend: Java 11+, Spring Boot, Spring Cloud, Hibernate, JPA.
  • Architecture: Microservices, Event-Driven Architecture, Domain-Driven Design (DDD), REST & GraphQL APIs.
  • Frontend: React, Redux, TypeScript, HTML5, CSS3.
  • Messaging & Streaming: Apache Kafka, RabbitMQ, ActiveMQ.
  • Databases: PostgreSQL, MongoDB, Redis, Cassandra.
  • DevOps: Docker, Kubernetes, Jenkins, GitHub Actions, Terraform, Helm.
  • Cloud Platforms: AWS / Azure / GCP (hands-on experience with architecture and deployment).

AI & Data Skills

  • Understanding of AI/ML fundamentals — model training, deployment, and inference.
  • Experience integrating AI services (OpenAI, AWS SageMaker, Azure Cognitive Services, Google Vertex AI).
  • Familiarity with NLP, computer vision, and data engineering concepts.
  • Exposure to MLOps pipelines and API-based AI solution integration.
  • Ability to identify business use cases for AI/ML within client environments.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
  • 15+ years of experience in software engineering, architecture, and delivery.
  • At least 5 years in a senior technical or solution architecture leadership role.
  • Proven expertise in Java, Microservices, React, Kafka, and exposure to AI/ML-driven applications.
  • Certifications preferred: AWS / Azure Architect, TOGAF, or AI-related specialization.


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