Job Summary We are seeking a Senior .NET Developer with strong frontend expertise in Angular or React to join our dynamic team. The ideal candidate will have deep experience in .NET Core, C#, Web API, relational/non-relational databases, and cloud platforms (Azure/AWS) while also being skilled in modern frontend development. This role requires designing and developing scalable, high-performance, and secure applications with a strong focus on frontend and backend integration. Key Responsibilities Develop, test, and maintain .NET Core applications and Web APIs. Build dynamic and responsive UI components using Angular or React. Ensure seamless frontend-backend integration with RESTful APIs. Design and implement scalable, secure, and high-performance applications. Work with relational (SQL Server, PostgreSQL, MySQL) and non-relational (MongoDB, DynamoDB) databases for efficient data management. Apply OOP principles, SOLID design, and software development best practices. Utilize Azure/AWS cloud services for deployment and management. Ensure code quality through unit testing, debugging, and performance optimization. Implement and manage Git for version control. Collaborate with cross-functional teams to define, design, and deliver new features. Troubleshoot and resolve technical issues to maintain application stability. Required Qualifications & Skills 4+ years of hands-on experience in .NET development. Strong proficiency in .NET Core, C#, and Web API. Proficient in ASP.NET development, including Windows applications, web applications, and WCF services. Experience in migrating ASP.NET applications from older versions to the latest versions, ensuring improved performance, security, and maintainability. Must have experience with frontend technologies Angular or React. Solid understanding of RESTful services, microservices architecture, and SOA. Experience with relational and non-relational databases (SQL Server, PostgreSQL, MySQL, MongoDB, DynamoDB). Hands-on experience with cloud platforms (Azure/AWS) and DevOps practices. Strong debugging, problem-solving, and troubleshooting skills. Familiarity with CI/CD pipelines, Docker/Kubernetes is a plus. Bachelor's degree in computer science, Software Engineering, or a related field. About TechGrit TechGrit is a custom software development services company dedicated to accelerating business growth through high-performing Agile teams. We specialize in delivering diversified and unique solutions across various industries, including Construction Tech, HealthTech, FinTech, and HighTech. Our team of champion developers works closely with clients to design and develop custom software solutions that meet their business needs. Our Mission To deliver high-quality and cost-effective software solutions that help companies and entrepreneurs achieve their goals and stay ahead of the competition. Our Vision To be the most trusted technology partner for our customers, accelerating their business growth through innovation, reliability, and excellence by the best-in-class people. Join TechGrit and be part of a team that values innovation, excellence, and teamwork. We are committed to creating an inclusive environment where all employees feel valued and respected. We would love to hear from you if you are passionate and want to contribute to our mission and vision.
Salary : 6 to 20 LPA We are seeking a Technical Writer with 1-3 years of experience in technical writing, preferably in domains such as AI, data science, machine learning, or complex engineering systems. This role is ideal for early-career professionals who are technically curious, detail-oriented, and eager to immerse themselves in the fast-evolving world of artificial intelligence. The ideal candidate brings a strong foundation in technical subjects with exceptional writing and communication skills. You will work closely with engineers, researchers, and product teams to produce high-quality, structured documentation that supports the usability, adoption, and understanding of our AI products and research. Key Responsibilities Technical Documentation : Create, edit, and maintain technical documentation, including requirement docs, user guides, API references, engineering notes, and product manuals Document machine learning workflows, NLP models, data pipelines, and research methods Translate academic-style research outputs into practical, accessible language for broader technical audiences Organize and structure information clearly to suit various user personas (engineers, researchers, product teams) Collaboration & Research Engage regularly with engineers, researchers, and data scientists to understand technical features and requirements Take part in technical discussions, sprint reviews, and product planning meetings to align documentation with evolving systems Investigate and synthesize multiple sources (e.g., code, papers, internal notes) into coherent documentation Content Development & Quality Build and follow documentation standards, style guides, and versioning practices Create and maintain templates and reusable content blocks to ensure consistency Review content for technical accuracy, clarity, grammar, and completeness Iterate based on feedback from users and contributors Required Qualifications Education : Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Physics, or a closely related technical field. 1-3 years of ex( can loop up to 4-5 years of exp at max) Core Competencies Outstanding written and verbal communication skills Strong ability to distill technical ideas into concise, well-structured writing Familiarity with core programming and data science concepts (e.g., Python, APIs, statistics) Excellent attention to detail, grammar, information architecture, and logical flow in writing Analytical mindset with the ability to learn and adapt quickly (ref:hirist.tech)
The job involves designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Building simulation frameworks grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Developing systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Researching fractal intelligence structures, swarm-based agent coordination, and autopoietic systems. Advancing Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integrating logic into distributed decision graphs aligned with business and ethical constraints. Publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory. Building scalable simulations of multi-agent ecosystems within the Mobius runtime. You should have a Ph.D. or M.Tech in Artificial Intelligence, Cognitive Science, Complex Systems, Applied Mathematics, or equivalent experience. Proven expertise in meta-learning, recursive architectures, and AI safety. Strong knowledge of distributed systems, multi-agent environments, and decentralized coordination. Proficiency in formal and theoretical foundations like Bayesian modeling, graph theory, and logical inference. Strong implementation skills in Python, additional proficiency in C++, functional or symbolic languages are a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is required. Preferred qualifications include experience with neurosymbolic architectures, hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, distributed learning systems like Ray, Spark, MPI, or agent-based simulators. Technical proficiency required in Python, preferred in C++, Haskell, Lisp, or Prolog for symbolic reasoning. Familiarity with frameworks like PyTorch, TensorFlow, distributed systems like Ray, Apache Spark, Dask, Kubernetes, knowledge technologies including Neo4j, RDF, OWL, SPARQL, experiment management tools such as MLflow, Weights & Biases, GPU and HPC systems like CUDA, NCCL, Slurm, and formal modeling tools like Z3, TLA+, Coq, Isabelle. Core research domains include recursive self-improvement and introspective AI, graph theory, graph rewriting, knowledge graphs, neurosymbolic systems, ontological reasoning, fractal intelligence, dynamic attractor-based learning, Bayesian reasoning, cognitive dynamics, swarm intelligence, decentralized consensus modeling, topos theory, autopoietic system architectures, teleo-dynamics, and goal-driven adaptation in complex systems.,